All Items
455 completed research items, newest first
Macro-level hallucination risk in schema-free GraphRAG clustering
How does the noisy baseline produced by unconstrained entity extraction corrupt the hierarchical summaries generated by standard Graph Retrieval-Augmented Generation (GraphRAG) community-detection pip…
Context collision and relational blindness in flat-vector RAG
Given that classical flat-vector Retrieval-Augmented Generation (RAG) acts as an external access mechanism rather than a persistent internal memory state, how do contradictory semantic overlaps in top…
Governance latency and contextual debt in AWS Context Ontology Accelerator…
To what extent does the human-in-the-loop governance requirement in the Amazon Web Services (AWS) Context Ontology Accelerator (COA) workflow exacerbate the stability-plasticity dilemma for agents con…
What constitutes cohesive and coherent organisational governance for aligned,…
What constitutes good cohesive and coherent organisational governance, meaning the specific configurations, principles, mechanisms, and performance thresholds that reliably produce aligned, high-veloc…
Decision governance for decentralized execution
How do large, established organizations deliberately design, implement, and continuously recalibrate the interdependencies among (1) decision governance systems (allocation of strategic versus operati…
How Do Enterprise AI Maturity Frameworks Map onto the LLM Consumption Ladder?
How do theoretical frameworks of enterprise Artificial Intelligence (AI) / generative AI maturity map onto the observed, practice-driven progression of large language model (LLM) consumption strategie…
Secure Runtime Evolution for AI Coding Agents
What is the logical progression in AI (Artificial Intelligence) coding-agent runtime design from local process/Operating System (OS) sandboxes, through shared Continuous Integration (CI)/cloud develop…
What is an Enterprise Architect?
What does an Enterprise Architect (EA) do, what do they explicitly not do, and how is the role distinguished from Business Architect and Domain Architect roles, including what they own, what they gove…
Governance and operating models for safe-to-fail experimentation in regulated…
In highly regulated industries such as financial services, healthcare, and pharmaceuticals, how do organisations design governance structures, team models, and operating practices that enable safe-to-…
Hybrid memory integration
How can Artificial Intelligence (AI) agents effectively synchronize structured semantic memory, meaning ontologies and knowledge graphs, with latent knowledge encoded in Large Language Model (LLM) wei…
Episodic-to-semantic memory consolidation in AI agents
What techniques enable AI agents to reliably generalize from specific episodic experiences (interaction logs, task traces, observed events) to durable semantic memory entries (ontological facts, proce…
Autonomous knowledge curation and truth maintenance for agentic ontologies
What mechanisms exist, or are under active research, to enable Artificial Intelligence (AI) agents to autonomously curate which extracted knowledge is worth retaining in a long-term ontology, detect a…
Privacy-preserving long-term memory for Artificial Intelligence agents
How can Artificial Intelligence (AI) agents preserve the utility of long-term memory for personalisation and historical context while enforcing privacy, security, and data-sovereignty controls strong…
Symbolic-connectionist synchronisation in hybrid agent memory
How can hybrid agent-memory architectures keep structured symbolic knowledge bases synchronised with unstructured Large Language Model (LLM) and retrieval-layer memory so that updates remain consisten…
Autonomous forgetting and information curation for long-term agent memory
How can Artificial Intelligence (AI) agents implement autonomous forgetting mechanisms and information-curation policies that preserve long-term memory utility while preventing retrieval quality, late…
Evaluation frameworks for agentic memory quality, relevance, and retrieval…
What benchmark suite and metric design best measures the quality, relevance, retrieval accuracy, freshness, and governance correctness of agentic memory systems across heterogeneous tasks?
Episodic-to-semantic memory consolidation architectures for agents
What architectures most effectively consolidate raw episodic traces into reusable semantic knowledge for Artificial Intelligence (AI) agents, and which triggers, review loops, and intermediate represe…
AWS AgentCore and AWS-native Knowledge Context Layer
What Amazon Web Services (AWS) AgentCore capabilities and AWS-native services are required to design and operate a Knowledge Context Layer (KCL) that continuously acquires, curates, evolves, and serve…
TBox-driven vs ABox-emergent ontology approaches in GraphRAG systems
To what extent do TBox (Terminological Box)-driven (predefined upper- and mid-level) ontologies outperform, underperform, or complement ABox (Assertion Box)-emergent (bottom-up, data-driven) approache…
Migration trade-offs from vector Retrieval-Augmented Generation to…
What are the performance, cost, scalability, and practical trade-offs of migrating from traditional vector-based Retrieval-Augmented Generation (RAG) systems to ontology-backed Knowledge Graph Retriev…
How should the balance between standardized and customized internal tooling…synthesis
How should the balance between standardized and customized internal tooling shift across industries, organisation sizes, maturity levels, and Artificial Intelligence (AI) agent adoption patterns, and…
What benefits, risks, and lifecycle costs of shadow Information Technology (IT)…
What benefits, risks, and lifecycle costs of shadow Information Technology (IT) and custom local tooling are documented, and which governance approaches successfully transition covert local solutions…
How do platform engineering, InnerSource, and standard-core plus…
How do platform engineering, InnerSource, and standard-core plus local-extension operating models balance team autonomy with organisational standardisation, and which patterns most reliably preserve l…
At what scale or under what operating conditions do the aggregate costs of…
At what scale or under what operating conditions do the aggregate costs of fragmented local tooling exceed the productivity gains from customization, and which metrics let organisations detect that cr…
How does local optimisation of team- and role-level tooling in knowledge work…
How does local optimisation of team- and role-level tooling in knowledge work reduce organisation-level throughput, and which interdependencies determine when local gains become global losses?
AI productivity, quality, and governance open questions
What empirical evidence can distinguish sustainable Artificial Intelligence (AI)-enabled software delivery gains from short-lived throughput effects and hidden quality or governance costs in productio…
TOGAF motivation architecture
What does The Open Group Architecture Framework (TOGAF)'s motivation architecture say about the dependency chain from business driver to goal to requirement: does it specify validation rules, or only…
SRE: establishing SLOs as contractual capability boundaries
How do Site Reliability Engineering (SRE) practices establish what a system can safely do, expressed as a contractual boundary rather than an observed average: specifically, how are Service Level Obje…
ITIL capacity management
What does IT Infrastructure Library (ITIL) capacity management specify as the measurement practice for establishing a platform capability baseline, and where does it rely on assertion rather than tele…
GORE: translating strategic intent to scoped delivery objectives
How does Goal-Oriented Requirements Engineering (GORE) handle the translation from strategic intent to scoped, time-bounded delivery objectives: what decomposition rules does it specify, and where do…
Goal specification: minimum schema and completeness validation
What properties must a Goal specification carry for an automated system to determine whether it is complete enough to act on -- specifically, what is the minimum schema, and what happens when fields a…
Model-based requirements engineering
Is there evidence from model-based requirements engineering on how scope changes to a Goal propagate to the constraint surface: specifically, does constraint re-enumeration happen automatically, or do…
Goal fragmentation: signals distinguishing salami-slicing from legitimate…
When a Goal is a fragment of a larger intent (salami-sliced deliberately or accidentally), what signals distinguish it from a legitimately scoped sub-goal?
Goal-constraint feedback
In control systems with feedback between goal definition and constraint measurement, what conditions cause the system to converge on a stable specification versus cycle without resolution, and what is…
Formal methods: specifying interdependent inputs for automated feasibility…
In formal specification methods (Z notation, Alloy, TLA+), how are systems with interdependent inputs specified so that an automated solver can determine feasibility without human arbitration at each…
Capability claim vs. production telemetry
When a team's capability claim conflicts with production telemetry, what arbitration mechanism produces a reliable baseline, and is there empirical evidence on which approach (telemetry override, stru…
Enterprise software pricing concessions, switching costs, and exit leverage
How do enterprise software vendors use upfront pricing concessions to increase switching costs over contract lifecycles, and what abstraction or architectural investment strategies demonstrably reduce…
How have software-development commit trends shifted across repository creation,…
What do high-quality longitudinal studies (2019–2026) show about directional shifts and current baseline ranges for repository creation rate, Lines of Code (LOC) velocity, rework share, project abando…
Q6: Leading indicators of instability in split-authority flow systems
Which metrics best predict unsafe queue growth, rising delivery risk, or hidden demand accumulation in a split-authority delivery system, where "split-authority" means a context in which authority is…
Q5: Control model for the best throughput-risk trade-off
When should the system use pre-approval, bounded delegation with guardrails, or post-hoc review and exception escalation?
Q4: Decision rights that should move closer to execution
Which decisions about sequencing, scope, reliability, technical debt, local spend, and incident response must sit with delivery teams to reduce delay without losing control?
Q3: Routing design that isolates exceptions from routine flow
What intake, triage, queueing, escalation, and routing model allows routine work to move quickly while isolating high-risk or ambiguous work?
Q2: Demand segmentation for fast-path vs controlled-path flow
Which work items are low-risk, standard, and reversible enough for fast-path handling, and which require slower expert review or tighter controls?
Q1: Dominant flow constraint in split-authority delivery systems
What is the dominant source of delay and instability in split-authority delivery systems: capacity shortage, dependency coupling, approval latency, funding gates, or fragmented decision rights?
A spl…
Operating model synthesis for split-authority delivery systemssynthesis
What operating model improves throughput while reducing delivery risk in a split-authority environment, where "split-authority environment" means a delivery context in which authority is divided among…
Plato's Forms and the 'Map Is Not the Territory'
What are the core tensions and possible symbiosis between Plato's claim that Forms are the truest reality and the modern semiotic claim that representations (maps, symbols, images) are not the territo…
AI-first software ecology in large engineering organisations (2025-2030)
What operating model, architecture strategy, and governance practices best improve developer productivity with Artificial Intelligence (AI) assistance in large software organisations, while preserving…
Domain Emergence in Semantic Networks, Cognition, and Organizational Structure
How do dense semantic graph structures, attractor-like concept stabilization, and distributed ownership or interpretation jointly drive the emergence and persistence of conceptual domains in enterpris…
Joint Embedding Predictive Architecture (JEPA) shift
Is the shift from text-token prediction to Joint Embedding Predictive Architecture (JEPA)-style video outcome prediction the same class of problem as the shift from video prediction to physically grou…
Ontology Completeness as a World Model for Large Language Model (LLM) Prediction
To what extent can a sufficiently complete ontology function as a practical world model (in the sense described by Yann LeCun) for Large Language Models (LLMs) making predictive inferences, and which…
LLM reasoning in mathematics and programming tasks
To what extent is the claim true that mathematics and programming are especially strong use cases for Large Language Models (LLMs) because both rely on formal symbolic languages that may align with mo…
Funding authority and delivery-risk accountability split
What governance and commercial structures best preserve delivery velocity, delivered quality, delivered risk control, delivery cost, and total cost of ownership when funding authority sits with a part…
Barriers to governance reform, leadership failure modes, and reform mechanisms…
What institutional and organisational barriers prevent effective governance reform in regulated enterprises, through what leadership failure modes are dysfunctional controls perpetuated, and by what m…
Failure mechanisms of internal governance controls
Through what mechanisms do internal governance controls in regulated enterprises transition from coordination cost minimisers to sources of bureaucratic inefficiency and informal circumvention, and wh…
Conditions under which internal governance controls minimise coordination costs…
Under what institutional and transaction-specific conditions do internal governance controls in regulated enterprises function as genuine minimisers of coordination costs rather than sources of bureau…
Similarity algorithms and growth policy for a file-based controlled theme…
Which similarity algorithms are appropriate for detecting near-synonym themes in a controlled vocabulary of 20–40 slug-based labels, and what growth policy prevents both vocabulary explosion and colla…
Long-term total cost of ownership trade-offs
How do structural differences between portfolios of a few large monolithic systems with tight
coupling, meaning many cross-component dependencies, and portfolios of many smaller tightly
cohesive syste…
Theory and mechanisms of prompt and program optimization in Language Models
What theory best explains why prompt and program optimization methods can outperform baseline prompting and Reinforcement Learning (RL) in Language Model (LM) pipelines, and how do the methods in the…
Contract theory formulation and statistical criteria for contracts
What is contract theory, how is a contract-theory model formally formulated, what is meant by a statistical contract, meaning a contract or protocol whose payoffs depend on statistical evidence, and w…
What capabilities, sub-capabilities, architectural patterns, and maturity…
What are the key capabilities, sub-capabilities, architectural patterns, and maturity dimensions for tool-using, semi-autonomous Semantic Knowledge Management (SKM) systems that integrate automated ha…
What is the Dynamic Resource Discovery architecture pattern in multi-agent…
What is the Dynamic Resource Discovery (DRD) architecture pattern in multi-agent systems, how does it relate to context engineering, meaning the design of what information enters an agent's working co…
What is the most practical enterprise design for a five-pillar knowledge…synthesis
What capability architecture, control model, and operating system of work best implement a five-pillar agentic, meaning tool-using and semi-autonomous, Knowledge Management (KM) model for Artificial I…
How should financial Retrieval-Augmented Generation (RAG) systems filter…
What pre-retrieval architecture and governance controls most reliably remove low-information content, meaning boilerplate, repeated passages, wrapper text, and other low-signal document fragments, and…
At what threshold does Human-in-the-Loop (HITL) oversight in bank compliance…
What measurable workload, alert-volume, and staffing thresholds indicate that Human-in-the-Loop (HITL) compliance review is no longer a meaningful challenge function, meaning reviewers mostly accept a…
How should banks detect and mitigate user-belief mirroring and sycophantic…
How do standard prompt-engineering patterns used in banking credit and compliance workflows trigger sycophancy, meaning model behaviour that agrees with user-stated beliefs over better-supported answe…
How should banks stop fluent but weakly evidenced Artificial Intelligence…
How does polished, authoritative generative Artificial Intelligence (AI) prose affect reviewer behaviour in anti-money laundering (AML) and Know Your Customer (KYC) workflows, and which interface and…
How should banks govern department-level agent sprawl and bottleneck shifts…
How does uncoordinated growth of department-level software agents change systemic risk and reporting integrity in banks, and which governance architecture can maintain consistency across agents, trace…
What Institutional Designs Create Low-Cost Help-Seeking Without Embarrassment,…
Which institutional design choices create persistently low-cost help-seeking spaces where workers can ask questions, admit uncertainty, and seek guidance without expecting embarrassment, punishment, o…
How Do Formal Governance Structures Distort Cross-Department Knowledge Flows?
How do formal governance mechanisms such as hierarchy, reporting lines, and mandatory protocols reshape cross-department knowledge flow, and when do they unintentionally raise cross-department transac…
Which Network Structures Bottleneck or Accelerate Knowledge Flow?
Which social-network topologies, the recurring patterns of ties among people, concentrate
knowledge flow into fragile bottlenecks, and which topologies enable fast cross-boundary
transfer of tacit kno…
What Are the Micro-Transaction Costs of Internal Knowledge Sourcing?
What micro-transaction costs are borne by knowledge seekers and providers during internal peer-to-peer transfers, and how do these costs shift the choice between self-solving ("make") and help-seeking…
Why Do Trust-Based Institutions Outperform Incentive Schemes for Knowledge…
Why do explicit transactional incentives for sharing often decay or backfire, while trust-based institutions, stable rules and norms that make repeated sharing safe and expected, sustain lower long-ru…
How Do Asset Specificity and Information Asymmetry Block Knowledge Transfer?
How do highly specific expert knowledge assets, knowledge investments whose value depends heavily on a particular context or relationship, create information asymmetries, situations where experts know…
How Do Activation-Energy Barriers, the threshold costs of starting a knowledge…
How do initiation costs such as evaluation anxiety, search effort, and access uncertainty act as a threshold barrier that suppresses knowledge seeking, and which organisational routines lower that thr…
The Complexity Horizon
In what ways does the Complexity Horizon of deeply nested, microservice-oriented classical architectures create an epistemic barrier where a deterministic system becomes just as uninterpretable and op…
State Space Explosion and Deterministic Chaos
How do state space explosion in concurrent systems and chaos theory, especially sensitive dependence on initial conditions, mirror the fragility of machine learning models when subjected to minor inpu…
The Halting Problem and Rice's Theorem
How do the Halting Problem (Turing) and Rice's Theorem formalise the absolute boundary of static analysis, proving that it is mathematically impossible to write a general algorithm to verify whether a…
Flexibility vs. Predictability
In a production pipeline with uncontrolled inputs, how does the trade-off between the flexibility of an agentic system and the predictability of a deterministic execution model affect the auditability…
Stochastic LLM Agent vs. Deterministic Coded System
How do the failure modes of a stochastic multi-step Large Language Model (LLM) agent, meaning a tool-using system whose action path can vary across runs, differ fundamentally from the failure modes of…
Formal Generalisation Bounds for Tool-Using LLM Systems When Tools Return…
What formal bounds can be stated for generalisation outside the training distribution in tool-using Large Language Model systems when their tools return non-deterministic outputs under unconstrained p…
Adversarial Input Propagation Through Multi-Step Tool-Using LLM Systems
How do adversarial inputs or unexpected environmental shifts propagate error through a multi-step tool-using Large Language Model (LLM) system's verification and strategy-selection phases when the und…
Agentic Tool-Feedback Loops and Explanatory Reach
When a Large Language Model (LLM) is wrapped in an agentic loop, meaning a repeated perception, strategy-selection, tool-action, and verification cycle, does the outer loop introduce true explanatory…
In-Context Learning and Chain-of-Thought Prompting
What are the empirical boundaries of in-context learning and chain-of-thought prompting when they are used to push a purely predictive statistical architecture toward intervention questions and altern…
The Stochastic Parrot Under Pressure
How does the Stochastic Parrot hypothesis, the claim that Large Language Models (LLMs) reproduce linguistic form more readily than grounded structural understanding, manifest when an LLM is presented…
Large Language Models as Statistical Optimisers
To what extent do Large Language Models (LLMs) optimise strictly for linguistic form and statistical token distribution rather than constructing internal, invariant causal models of reality?
Pearl's Causal Hierarchysynthesis
What are the formal information-theoretic boundaries that prevent a model trained exclusively on observational data (Level 1 on Pearl's Ladder of Causation) from ever executing or predicting the outco…
Structural Stability vs. Predictive Fragility
Using dynamical systems theory, how does the fragility of a purely predictive model under input noise or system drift differ from the local qualitative stability of a model whose governing equations p…
The Duhem-Quine Thesis and Underdetermination
How can the phenomenon of multiple distinct functions perfectly interpolating identical data points be formalised through the lens of the Duhem-Quine thesis, underdetermination of theory by data, and…
Empirical Risk Minimisation's Causal Blindness
How does the framework of Empirical Risk Minimisation (ERM) mathematically guarantee predictive accuracy within a known data distribution while remaining blind to the stable cause-and-effect relations…
Failure Modes of Instrumentalist Epistemology When Applied to Complex Dynamic…
What are the operational failure modes of an epistemic framework that prioritises instrumentalism, treating predictive performance as the primary criterion, over explanatory reach when applied to comp…
David Deutsch's Hard-to-Vary Criterion
Using David Deutsch's hard-to-vary criterion, meaning an explanation whose details cannot be changed without losing explanatory force, what formal criteria can measure the internal logical constraints…
Formalising Popper's Falsifiability as a Mathematical Criterion for…
How can Karl Popper's criterion of demarcation and falsifiability be mathematically formalised to distinguish between a model that explains a physical mechanism and one that merely interpolates observ…
What Are We Losing and Gaining by Inserting Autonomous Tool-Using Artificial…synthesis
What are we concretely losing and gaining, across the dimensions of capability, reliability, auditability, explainability, and organisational risk, by inserting autonomous tool-using Large Language Mo…
Are Multi-Step Large Language Model-Based Systems Inherently Less Explainable…synthesis
Are multi-step Large Language Model (LLM)-based systems inherently less explainable than equivalently scoped deterministic software systems, or does production-scale distributed-system complexity make…
Visibility and exit outcomes
How often does vendor-supplied temporary operational automation produce materially worse visibility and exit outcomes than internally governed temporary operational automation?
Longitudinal persistence rates after gap closure for low-code applications,…
What longitudinal evidence exists on persistence rates after the original gap is closed for low-code applications, bots, and agents in live enterprise estates?
Datasets for measuring conversion from demand for local workaround tools to…
What public or internal datasets can validly measure the rate at which demand for local workaround tools, such as local apps, flows, lists, or spreadsheets, is converted into formal central Informatio…
Governance designs where explicit integrator rights substitute for co-location…
Under which governance designs do explicit integrator rights fully substitute for structural co-location of risk, cost, and benefits, and under which conditions do these designs fail?
Matched denominator for comparing post-pipeline release-based failures with…
What common denominator enables direct matched comparison between post-pipeline release-based failure rates and production live-runtime incident rates for the same production workflow?
LLM-First Policy Clarification and Institutional Knowledge Atrophy
How does shifting from peer policy clarification to Large Language Model (LLM)-first interaction affect institutional memory transfer, mentoring, and long-term policy expertise?
Policy Quality Degradation and Cross-Institution Blind Spots When New Policy…
What policy-quality degradation and systemic blind-spot risks emerge when organisations draft new policy versions from Large Language Model (LLM) interpretations of previous policy versions?
LLM Response Style and Confidence Signalling
How do Large Language Model (LLM) response style and self-reported confidence change how accurately users judge uncertainty and downstream risk when interpreting ambiguous policy and compliance requir…
LLM Training Prior Contamination in Compliance Interpretation
What failure modes emerge when Large Language Models (LLMs) combine generic public legal knowledge with proprietary organisational policy in compliance interpretation tasks?
Cognitive Closure Under Ambiguity and Confirmation Bias
How do pressures to reach a quick, definite answer under ambiguity and iterative prompt refinement influence acceptance of flawed Large Language Model (LLM) policy interpretations?
De Facto Policy Drift From Repeated Unverified LLM Interpretations
How quickly do repeated unverified Large Language Model (LLM) interpretations create de facto policy norms that diverge from executive intent and board-level risk appetite?
Adversarial prompting risks in policy assistants
How vulnerable are corporate compliance Large Language Models (LLMs) to adversarial prompting that reframes restrictive policy as permissive guidance, and which controls detect or contain deliberate m…
AI-Assisted Policy Interpretation and Accountability Displacement
How does integration of Large Language Models (LLMs) into policy-ambiguity resolution change liability allocation, escalation behaviour, and an organisation's ability to justify the resulting decision…
Policy enforcement and formal verification as Energy-Based Model (EBM)…
How can discrete policy engines and formal verifiers be translated into continuous or structured optimization signals that guide Energy-Based Model (EBM) search while preserving the original natural-l…
Layered reasoning stack interfaces
What state abstraction boundaries and interface protocols are most effective for mapping Large Language Model (LLM) candidate outputs into Energy-Based Model (EBM) evaluation state spaces while preser…
Kona and Aleph at their core, with Lean and unifying concepts
What are Kona and Aleph at their core, what do they each do in practice, how does Lean (the theorem prover) relate to them, and which unifying concepts explain where they overlap and differ?
ServiceNow Artificial Intelligence (AI) Control Tower
What is the complete set of features, functions, and capabilities offered by ServiceNow AI Control Tower, and how do those capabilities address enterprise Artificial Intelligence (AI) governance, obse…
Microsoft Copilot Studio
What is the complete set of features, functions, and capabilities offered by Microsoft Copilot Studio, and how do those capabilities support enterprise-grade Artificial Intelligence (AI) agent develop…
Microsoft Foundry (formerly Azure Artificial Intelligence (AI) Foundry)
What is the complete set of features, functions, and capabilities offered by Microsoft Foundry, and how do those capabilities support the full Artificial Intelligence (AI) development lifecycle, from…
Amazon Web Services (AWS) Bedrock platform capabilities
What is the complete set of features, functions, and capabilities offered by Amazon Web Services (AWS) Bedrock, including its model access, agent building, knowledge bases, guardrails, evaluation, and…
Amazon Bedrock AgentCore and related suite
What is the complete set of features, functions, and capabilities offered by Amazon Bedrock AgentCore and its related suite, including AgentCore Gateway, AgentCore Memory, AgentCore Identity, and the…
Governance structures that support investment in delivery capability without…
Under what governance conditions can investment in building durable delivery capability be made reliably without placing risk, cost, and benefits accountability under one owner, and what minimum autho…
Variance Control Comparison Across Delivery Modes
What is the empirical failure-rate distribution of Artificial Intelligence (AI)-assisted code that has passed a standard software delivery pipeline compared with AI-agent-executed business processes a…
Information Technology (IT) throughput capacity as a constraint on unmet…
What is the empirical relationship between Information Technology (IT) throughput capacity and the rate at which unmet operational capability needs accumulate across comparable organisations, and what…
Automated decommission of temporary bridge Artificial Intelligence (AI) agents
What technical and organisational mechanisms most reliably cause temporary bridge Artificial Intelligence (AI) agents to be decommissioned when the corresponding software capability is delivered, and…
External Dependency Surface Taxonomy for Production LLM Agents
What is the complete taxonomy of external dependencies for a production Large Language Model (LLM)-based agent, how does each dependency class fail, what is the blast radius of each failure class, and…
Temporary Automation Demand Persistence and Core Capability Investment…
What evidence exists that temporary automation workarounds displace investment in core software delivery, and what is the observed persistence rate of those workarounds after the underlying systems ca…
Agent Operational Cost vs Gap Closure Cost
What is the fully loaded operational cost of a production Artificial Intelligence (AI) agent used as a workaround for a missing system capability, relative to the cost of closing the underlying system…
Reference architecture definition, framework landscape, and required detail…
What should a practical reference architecture include, which established architecture frameworks define or structure it, and how much detail should be specified across capabilities, components, flow…
Universal Entity Lifecycle Governance Framework (UELGF) 8-layer organisational…
What is the most suitable knowledge representation architecture for evolving the Universal Entity Lifecycle Governance Framework (UELGF) 8-layer organisational context model from static classification…
Ontology landscape for curated lexical and structured enterprise context
For a curated corpus that mixes lexical documents, structured artifacts, application programming interface (API) landscapes, access controls, infrastructure definitions, schemas, and process documenta…
Declaration of the Independence of Cyberspace
What are the historical origins and core claims of John Perry Barlow's *Declaration of the Independence of Cyberspace*, how have those claims influenced modern research and technology governance, and…
Empirical evidence on rollout of organisation-wide low-code and no-code programs
What does peer-reviewed and independently verified empirical evidence reveal about the outcomes, success factors, governance models, and failure modes of organisation-wide low-code or no-code (LCNC) p…
Vendor Non-Compliance With or Absence of Implementation Standards
What failure modes have been empirically observed in organisations where vendors do not comply with established implementation standards, or where implementation standards are absent or insufficiently…
Separated Risk, Cost, and Benefits Accountability Across Business Units
What failure modes have been empirically observed in organisations where accountability for risk, operational cost, and benefits realisation are held in separate business units (BUs) rather than co-lo…
Project-Based Demand Governance With Product-Structured IT Teams
What failure modes have been empirically observed in organisations where demand is managed through a project-based model while information technology (IT) teams are structured and operated as product…
Customer-Segment Demand Prioritisation Against Domain-Based IT Teams
What failure modes have been empirically observed when organisations prioritise information technology (IT) work through customer segments, for example consumer, enterprise, or government cohorts, but…
Overlapping and Absent Accountability at Strategic and IT Layers
What failure modes have been empirically observed in organisations where accountability is either overlapping, two or more parties hold the same accountability, or absent, no party owns a given area,…
PromptQL definition, research foundations, and related technologies
What is PromptQL, what active research areas are most closely related to it, what prior research foundations PromptQL appears to build on, and which adjacent technologies should be considered when eva…
Endsley Model of Situational Awareness deep dive
What is the Endsley Model of situational awareness, meaning the perception of relevant elements, comprehension of their meaning, and projection of their near-future status, how are its three levels de…
What is Anthropic's '4D' framework for Artificial Intelligence (AI) fluency,…
What is Anthropic's "4D" framework for Artificial Intelligence (AI) fluency, what do each of the four Ds, Delegation, Description, Discernment, and Diligence, mean in practice, and how does this frame…
Architectural patterns for reliable organizational process identification,…
What integrated architectural configuration of retrieval, reconciliation, constraint enforcement, memory, validation, escalation, and governance mechanisms most reliably enables visual workflow toolin…
Graph database landscape
For the hosted graph database platforms identified in the 2026 Software-as-a-Service (SaaS) knowledge-ontology research, Neo4j AuraDB, Amazon Neptune, Stardog Cloud, Ontotext GraphDB, and Memgraph Clo…
Agent-to-Agent (A2A)-to-tool-calling unification
To what extent does unifying specialised Agent-to-Agent (A2A) protocols into a standardised tool-calling interface affect orchestration overhead and reasoning accuracy in hierarchical multi-agent syst…
When Retrieval-Augmented Generation source documents change after agent build…
When the source documents indexed in a Retrieval-Augmented Generation (RAG) pipeline change after an agent has been built and tested, what failure modes and behavioral regressions can result in produc…
Web ontologies in production Knowledge Graphs for multi-step Artificial…
How should web ontologies, Resource Description Framework (RDF), Web Ontology Language (OWL), RDF Schema (RDFS), Simple Knowledge Organization System (SKOS), and Schema.org, be selected, composed, and…
Open Digital Rights Language (ODRL) policies in Knowledge Graphs for…
How can the World Wide Web Consortium (W3C) Open Digital Rights Language (ODRL) be used to encode access control, usage policies, and governance constraints within or alongside a Knowledge Graph (KG)…
Knowledge Graph lifecycle management for multi-step software agents
What are the best practices for maintaining and evolving a Knowledge Graph (KG), a structured graph of entities and relationships, that serves multi-step software agents, covering schema versioning, e…
Knowledge Graph as a data product
What does it mean to treat a Knowledge Graph as a data product in a data mesh architecture, and how should data product principles, including domain ownership, data contracts, discoverability, interop…
Knowledge Graph in the live execution path of multi-step Large Language Model…
What architectural patterns, operational practices, and failure modes arise when a Knowledge Graph (KG) becomes a key part of the live execution path for multi-step Large Language Model (LLM) systems,…
Data product ontology
What is the data product ontology, which organisations and communities use it, how is it applied in practice within data mesh and data management architectures, and is it still current relative to com…
International Organization for Standardization (ISO) and International…
What is International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) 42001:2023 for an Artificial Intelligence Management System (AIMS), and which specific…
Hardware load and Large Language Model (LLM) inference performance
How does hardware resource load, Central Processing Unit (CPU), Graphics Processing Unit (GPU), and memory pressure, affect Large Language Model (LLM) inference performance, specifically latency, thro…
Hosted Software-as-a-Service (SaaS) graph database options for knowledge…
Which hosted Software-as-a-Service (SaaS) graph database platforms are suitable for building and querying a knowledge ontology, and how do they compare on data model support, query language, pricing,…
Security, Compliance, and Governance Risks of Using Generative AI (GenAI) Tools…
What are the documented security, compliance, and governance risks of using Generative Artificial Intelligence (GenAI) tools such as Microsoft 365 (M365) Copilot for drafting memos, reports, and other…
Control deficiencies from bypassing designated workforce record platforms
What control deficiencies are most common when designated workforce record platforms are bypassed by spreadsheet, presentation, and list-based shadow workflows?
Taxonomy criteria: process inefficiency versus hidden control and dependency…
Which explicit criteria best distinguish ordinary process inefficiency from hidden control and dependency risk in workforce-capacity and skill-tracking workflows?
Process-Risk-Control (PRC) scoring impacts from unstandardized workforce…
How should inherent risk, meaning exposure before relying on controls, and control effectiveness, meaning the demonstrated reliability of the mitigating control, scores in a PRC library change when wo…
Language Server Protocol (LSP)-style policy surfaces and workforce taxonomies…
How can workforce-capacity and skills-taxonomy structures integrate with a Language Server Protocol (LSP)-style policy diagnostic surface to detect persistent capability mismatches automatically in en…
National Institute of Standards and Technology (NIST) Special Publication (SP)…
How do missing provenance, lineage, and change-history controls in Microsoft Lists, Excel, and PowerPoint workforce artifacts conflict with NIST SP 800-53 Rev. 5 integrity-related controls?
Key-person dependency and Basel execution, delivery, and process-management…
How should key-person dependency in workforce-critical processes be mapped to execution, delivery, and process-management risk categories in Basel Committee framing?
Control Objectives for Information and Related Technologies (COBIT) and…
What minimum process-definition conditions do COBIT 2019 and CMMI require before mitigation of workforce-process risk can be considered effective and sustainable?
Basel Committee on Banking Supervision (BCBS), International Organization for…
How do Basel Committee on Banking Supervision (BCBS), International Organization for Standardization (ISO) 31000, and National Institute of Standards and Technology (NIST) frameworks classify risk whe…
Implementation Patterns for Regulatory Compliance in Artificial…
What specific implementation patterns, including externalized machine-executable policy rules (Policy-as-Code (PaC)), rules engines, input, tool-use, and output safety controls (guardrails), output va…
Practical Limits of Large Language Model (LLM) Determinism
What are the practical limits of making LLM (Large Language Model)-based decisions or policy enforcement deterministic, even with temperature=0, fixed seeds, and constrained prompts?
Hybrid Architecture Design
How should hybrid architectures be designed so that probabilistic LLMs handle interpretation and insight generation while deterministic layers enforce final governance, compliance, and high-stakes dec…
Governance Policy Application
To what extent must governance policy application be deterministic, consistent, reproducible, and auditable, versus allowing stochastic or probabilistic elements when Artificial Intelligence (AI) or L…
Data Governance Standards and Regulations Applied to Artificial Intelligence…
How do established data governance standards, including International Organization for Standardization and International Electrotechnical Commission (ISO/IEC) 38505, DAMA-DMBOK (Data Management Body o…
Extending Traditional Data Governance Frameworks to Address Large Language…
How can traditional data governance frameworks be extended or mapped to address the inherent non-determinism and uncertainty about whether deployed behavior remains aligned with intended use in modern…
Compliance Risks of Relying on Stochastic Large Language Model (LLM) Outputs…
What evidence or guidance exists on the compliance risks of relying primarily on stochastic Large Language Model (LLM) outputs for governance, privacy, or regulatory decisions?
Build vs improve tradeoff
Given constrained engineering capacity, how should organisations allocate effort between (1) building features within an existing system and (2) improving the system itself (tooling, process, architec…
Orthogonality thesis under modern Large Language Model (LLM) training and…
How should the orthogonality thesis be interpreted for modern Large Language Models (LLMs) given current pre-training and post-training methods, and what does that imply for enterprise risk when agent…
What are the primary behavioural and structural drivers of unsanctioned AI…
What are the primary behavioural and structural drivers of shadow Artificial Intelligence (AI) adoption, meaning unsanctioned use of AI tools without formal approval or oversight, in enterprises after…
What tiered human oversight models maintain meaningful human-in-the-loop (HITL)…
Under high-volume deployment of multi-step Artificial Intelligence (AI) systems, what factors cause human-in-the-loop (HITL) oversight to degrade into rubber-stamping, meaning approval without genuine…
What metrics beyond code acceptance rates best capture net organisational value…
What metrics beyond code acceptance rates and lines of code best capture net organisational value when Artificial Intelligence (AI) coding tools such as GitHub Copilot are adopted with productivity ma…
How do coupled enterprise risks manifest differently in agentic Artificial…synthesis
How do the coupled enterprise risks, capability debt, incentive-driven shadow Artificial Intelligence (AI) adoption, skill decay, and oversight failure, manifest differently in agentic AI, meaning aut…
How can organisational capability debt be rigorously defined and measured as a…
How can capability debt, the accumulated organisational deficit in review quality, judgment, process maturity, and skill inventory, be rigorously defined, measured, and tracked as a leading indicator…
To what degree does over-reliance on AI tools accelerate measurable skill decay…
To what degree and through what mechanisms does over-reliance on Artificial Intelligence (AI) tools, particularly tools that can plan or act across multi-step workflows, accelerate measurable skill de…
Updating the enterprise Artificial Intelligence ecosystem capability reference…synthesis
How should the enterprise Artificial Intelligence (AI) ecosystem capability reference architecture (as expressed in `2026-04-22-enterprise-ai-capability-model`, `2026-05-05-enterprise-ai-capability-st…
Production incidents linked to Artificial Intelligence systems
What documented production incidents over the last five years were caused or materially contributed to by Artificial Intelligence (AI) systems, and what recurring failure modes and mitigations were id…
Artificial Intelligence (AI) regulatory guidance delta checksynthesis
Since the completion of `2026-04-24-ai-agent-regulation-global-financial-services`, what newly issued regulatory advice, policy, guidance, or supervisory statements have been published on Artificial I…
Five Eyes stance on Artificial Intelligence risk and policy advice
What is the current stance of the Five Eyes intelligence alliance (Australia, Canada, New Zealand, United Kingdom, United States) on Artificial Intelligence (AI) risks, and what concrete policy and op…
Integrating 2026-05 security and supply chain findings into the enterprise…synthesis
How should the enterprise Artificial Intelligence (AI) ecosystem capability reference architecture (as expressed in `2026-04-22-enterprise-ai-capability-model` and the `2026-05-05-enterprise-ai-capabi…
What is the architecture and practical applicability of OpenFactCheck as an…
What is the architecture, evaluation methodology, and practical applicability of OpenFactCheck as an automated, modular, claim-level fact-checking pipeline for Artificial Intelligence (AI)-generated c…
What are the capabilities, architectural assumptions, and practical deployment…
What are the capabilities, underlying architectural assumptions, and practical deployment constraints of Loki as an MIT-licensed automated fact-checking tool optimised for journalists and content mode…
How do open-weight policy enforcement reasoning models, exemplified by OpenAI's…
How do open-weight, meaning released-weight and self-hostable, policy enforcement reasoning models, exemplified by OpenAI's gpt-oss-safeguard, classify text against strict, customizable policies, and…
How does Factual precision Scoring (FActScore) operationalise atomic-level…
How does FActScore (Factual precision Scoring), developed at the University of Washington, operationalise the concept of atomic factual claim decomposition and precision scoring for Large Language Mod…
How can findings from OpenFactCheck, Loki, FActScore, gpt-oss-safeguard, and…synthesis
How can the findings from research into OpenFactCheck, Loki, FActScore, gpt-oss-safeguard, and Barnum statement identification techniques be synthesised into concrete, actionable improvements to the a…
What are Barnum statements (Forer Effect statements), how do they manifest in…
What are Barnum statements (also known as Forer Effect statements) as a class of vague, universally applicable assertions, how do they manifest specifically in Artificial Intelligence (AI)-generated r…
What is the minimal viable schema for an Artificial Intelligence bill of…
What is the minimal viable set of schema properties required to describe Artificial Intelligence (AI) system dependencies for systems that use prompts, retrieval knowledge bases, memory, and tools in…
Why does Software Bill of Materials (SBOM) fail as a complete inventory model…
Why do traditional Software Bill of Materials (SBOM) concepts fail to adequately describe the dependency, provenance, and runtime composition of agentic Artificial Intelligence (AI) systems, and what…
How can a runtime-observed Artificial Intelligence Bill of Materials (AIBOM) be…
How can a dynamic, runtime-observed Artificial Intelligence Bill of Materials (AIBOM) be generated for an agentic Artificial Intelligence (AI) system, capturing execution traces, transient Retrieval-A…
How do you capture a runtime-observed Artificial Intelligence Bill of Materials…
How do you instrument a real agentic Artificial Intelligence workload, meaning a tool-using workload that plans or acts across multiple steps, to capture a runtime-observed Artificial Intelligence Bil…
How does the European Union (EU) AI Act and related international AI governance…
- [fact; source: https://owaspaibom.org/] Artificial Intelligence Bill of Materials (AIBOM) is used here in the Open Worldwide Application Security Project (OWASP) sense of an artifact intended to mak…
What introspection, export, and control surfaces actually exist across…
What logs, traces, audit Application Programming Interfaces (APIs), Artificial Intelligence Bill of Materials (AIBOM) export capabilities, version-pinning mechanisms, allowlists, and policy hooks actu…
How should identity, delegation chains, and permission scopes be formally…
How should identity, delegation, and permission scopes be formally represented in an Artificial Intelligence Bill of Materials (AIBOM) schema to enable end-to-end attribution, "who authorized what", a…
How do OAuth 2.0, OpenID Connect, and SPIFFE token propagation work in real…
How do OAuth 2.0 (Open Authorisation), OpenID Connect (OIDC), and SPIFFE (Secure Production Identity Framework for Everyone) token propagation mechanisms work in real multi-agent Artificial Intelligen…
What security and governance risks can a declared and runtime-observed…synthesis
What categories of security and governance risk can an Artificial Intelligence Bill of Materials (AIBOM), an artifact intended to make artificial intelligence systems transparent, auditable, and secur…
How do you construct a declared design-time Artificial Intelligence Bill of…
How do you extract and construct a declared design-time Artificial Intelligence Bill of Materials (AIBOM), covering model, prompt or system instruction, tools, Retrieval-Augmented Generation (RAG) kno…
What measurement systems and frameworks exist for quantifying Information…
What measurement systems and frameworks exist for quantifying Information Technology (IT) system legibility, defined here as the ability to reason about, understand, and comprehensively characterise t…
What does the 2026 Harvard Business Review trendslop study and related…
What does the March 2026 Harvard Business Review (HBR) "trendslop" study reveal about positional bias, prompt-framing sensitivity, and context-insensitive bias in Artificial Intelligence (AI)-generate…
What systematic review methodologies and Artificial Intelligence (AI)-assisted…
What systematic review methodologies, Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), Cochrane review, narrative synthesis, meta-ethnography, and realist synthesis, and wh…
How does STORM's perspective discovery step work, and what is the…
How does the STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective question generation) system's perspective discovery step generate diverse expert viewpoints before decomposing a…
What structured approaches and Artificial Intelligence (AI) agent workflow…
What structured approaches, from academic writing pedagogy, Artificial Intelligence (AI)-assisted writing tools, and agent workflow design, exist for converting synthesised research findings into poli…
What are the established norms from academic pre-print repositories and…
What are the established norms and practical conventions from academic pre-print repositories (arXiv, Social Science Research Network (SSRN), Open Science Framework (OSF)) and Personal Knowledge Manag…
What entity-relation schema and write/query patterns best support cross-session…
What entity-relation schema and write-query prompt patterns best support cross-session research provenance and concept reuse for an Artificial Intelligence (AI) research agent using the `@modelcontext…
What structured knowledge-gap tracking and automatic backlog-promotion patterns…
What structured knowledge-gap tracking and automatic backlog-promotion patterns exist in Personal Knowledge Management (PKM) systems (linked-note methods such as Zettelkasten, Obsidian, Roam Research,…
What technical architecture best supports cross-item synthesis, knowledge…
What technical architecture best supports three distinct but related capabilities in a file-based research corpus (~200 items, growing weekly): (1) a meta-distillation layer that proactively aggregate…
What automated claim verification approaches against scientific literature…
What automated claim verification approaches against scientific literature, specifically arXiv preprints, are used in research synthesis systems, what search strategies maximise recall and precision f…
What adversarial review and red-teaming methods are most effective for…
What adversarial review and red-teaming methods, drawn from Artificial Intelligence (AI) safety research, debate-based evaluation, formal argumentation theory, and scientific peer review practice, are…
What architectural capabilities and contractual conditions are required to…
What architectural capabilities and contractual conditions are required for an enterprise to maintain multi-platform portability and mitigate Artificial Intelligence (AI) vendor lock-in risk from: Mic…
What capability and control design is needed to mitigate incentive…
What capability and control design is needed, at enterprise scale, to mitigate incentive misalignment (where individuals are rewarded for bypassing governance), shadow Artificial Intelligence (AI) (AI…
How should human-in-the-loop (HITL) design be adapted when AI review volume…
How should human-in-the-loop (HITL) design be adapted when Artificial Intelligence (AI) review volume reaches the point where human reviewers become a throughput bottleneck or default to rubber-stampi…
What security capabilities are required in an enterprise Artificial…
What security capabilities are required in an enterprise Artificial Intelligence (AI) system, beyond basic Application Programming Interface (API) access controls and audit logging, to address prompt…
Vendor-agnostic enterprise Artificial Intelligence (AI) capability model
What is the complete set of architectural capabilities required to run Artificial Intelligence (AI) safely at scale in a regulated enterprise, how do Microsoft's Copilot family (Microsoft 365 Copilot…
What does TerminalBench reveal about minimal toolsets and coding agent…
What does the TerminalBench benchmark reveal about the relationship between toolset minimalism and coding agent performance, and what design principles does it suggest for effective Artificial Intelli…
What principles and governance practices enable sustainable, high-quality…synthesis
What principles and governance practices, spanning harness design, task selection, human oversight, and open-source software (OSS) ecosystem health, enable sustainable, high-quality software developme…
What are the design tradeoffs of self-modifying, malleable Artificial…
What are the design tradeoffs, in capability, reliability, safety, and maintainability, between self-modifying agent architectures, where the agent can alter its own toolset, prompts, or extensions at…
What strategies are effective for open-source software maintainers dealing with…
What strategies are effective for open-source software (OSS) maintainers in filtering, managing, and sustaining project health against a rising volume of low-quality Artificial Intelligence (AI) agent…
What is the evidence for human oversight as an effective quality gate in…
What is the empirical evidence that human oversight, specifically the human bottleneck property of limited throughput and pain response, functions as an effective quality gate, meaning the control poi…
What design patterns govern effective extension and plugin systems for…
What design patterns and architectural principles govern effective extension and plugin systems for Artificial Intelligence (AI) coding agent harnesses, and what are the key trade-offs between extensi…
How do errors compound in Artificial Intelligence (AI)-agent-heavy codebases,…
How do errors ("boooos") compound in codebases developed with high volumes of AI agent-generated code, including how local patches cause global regressions, and what review and governance strategies c…
What are best practices for transparent, user-controlled context management in…
What are the best practices for transparent, deterministic, and user-controlled context management in Large Language Model (LLM) coding agent harnesses, and what are the demonstrable harms of opaque c…
What criteria define tasks where Artificial Intelligence (AI) coding agents…
What empirically grounded criteria define the characteristics of software development tasks where Artificial Intelligence (AI) coding agents reliably add value, versus tasks where agent autonomy intro…
Artificial Intelligence coding harness quality benchmarks
What benchmarks, metrics, and evaluation methodologies are used to measure the quality of Artificial Intelligence (AI) coding harnesses, including Integrated Development Environment (IDE) plugins, age…
Prof Suraj Srinivasan's automation and augmentation scores
What does Prof Suraj Srinivasan's research framework for measuring Automation and Augmentation (A&A) scores across job roles and industries reveal about which roles face full Artificial Intelligence (…
Ubiquitous Language in Artificial Intelligence (AI)-augmented development
How significantly does maintaining a living Ubiquitous Language (UL), in the Domain-Driven Design (DDD) sense of a shared, precise domain vocabulary used consistently in both code and conversation, im…
Test-Driven Development (TDD) and fast feedback loops in Artificial…
How does enforcing Test-Driven Development (TDD) with AI coding assistants, writing failing tests before asking the AI to implement, change the quality and stability of the AI output compared to "writ…
Strategic versus tactical roles in Artificial Intelligence (AI)-augmented…
In an Artificial Intelligence (AI)-augmented software team, what is the optimal division of labour between the human developer, who owns strategic design, interface definition, and architectural overs…
Software Engineering fundamentals and AI code generationsynthesis
Drawing on the planned seven-item research programme on Software Engineering (SE) fundamentals in Artificial Intelligence (AI)-augmented development, six completed primary items plus external anchors…
Grill-Me technique: iterative structured interviewing for human and Artificial…
How effectively does the "Grill Me" technique, relentless iterative structured interviewing of the human developer by the AI assistant to build a shared design concept before generating any code, redu…
Fundamentals-first versus specs-to-code
What empirical patterns emerge when comparing real-world software projects built with a strict fundamentals-first Artificial Intelligence (AI) workflow, structured alignment, modules with simple inter…
Deep modules in AI-augmented development
How much more effective is Artificial Intelligence (AI) at understanding, navigating, and correctly modifying a codebase composed of deep modules with simple interfaces versus one filled with many sha…
Artificial Intelligence code entropy and complexity
Does repeated Artificial Intelligence (AI) code generation without strong architectural guardrails demonstrably increase software entropy and complexity over time, as predicted by the entropy model de…
Deterministic weighted scoring models for customer risk rating under MLR 2017
To what extent do deterministic weighted scoring models (based on the four main risk factors: customer, geographic, product/service, and delivery channel) effectively support a proportionate risk-base…
Anthropic Claude Teams or Enterprise vs Microsoft 365 Copilot Coworksynthesis
How do Anthropic Claude, specifically the Team and Enterprise plans, and Microsoft 365 (M365) Copilot Cowork compare across capability, pricing, user experience, and guardrails, and what are the secur…
The orthogonality thesis in Artificial Intelligence (AI) alignment
What is the orthogonality thesis in Artificial Intelligence (AI) alignment, what is the current evidence for and against it, and what are its practical implications for Explainable Artificial Intellig…
Human cognitive bias toward Artificial Intelligence (AI) correctness and…
To what extent do humans systematically over-trust AI-generated explanations, and what mechanisms, automation bias, RLHF-induced sycophancy in post-training, and the polysemantic nature of internal mo…
Explainable Artificial Intelligence (XAI)
What is the current state of Explainable Artificial Intelligence (XAI) research, who leads it and what are the primary techniques, and how does XAI intersect with regulatory obligations, audit require…
Is knowledge scaffolding an established concept within context engineering for…
Is knowledge scaffolding an established concept within context engineering for Large Language Models (LLMs) and Artificial Intelligence (AI) agents, and if so, how is it defined, implemented, and dist…
Which software categories face declining demand versus increasing demand as…
As Artificial Intelligence (AI) coding agents, such as Anthropic Claude Code, OpenAI Codex, and GitHub Copilot Workspace, make custom software generation materially cheaper, which categories of commer…
Large Language Model (LLM)-as-judge as pipeline validation checkpoints
Which organisations, projects, and frameworks are defining and operationalising Large Language Model (LLM)-as-judge evaluation, the use of one model to assess another model's outputs, as automated val…
Alternative Continuous Integration and Continuous Delivery pipeline platforms…
What alternative Continuous Integration and Continuous Delivery (CI/CD) pipeline platforms, specifically Harness, Amazon Web Services (AWS) CodeBuild and CodeDeploy, and Jenkins, can serve as the gove…
Universal Entity Lifecycle Governance Framework (UELGF) extension
What concrete reference architecture and tooling specification, covering policy-as-code engines such as Open Policy Agent (OPA) and Cedar, observability pipelines such as OpenTelemetry (OTel), and mod…
Universal Entity Lifecycle Governance Framework (UELGF) extension
What explicit human oversight and accountability requirements, covering named human owners for every governed entity, defined escalation paths for high-risk autonomous actions, accountability designat…
Universal Entity Lifecycle Governance Framework (UELGF) extension
What agentic Artificial Intelligence (AI)-specific risk categories, specifically emergent behaviour, goal misalignment, multi-agent interaction failures, and hallucinations in decision loops, are insu…
How do academic and scientific publishing systems handle post-publication…
How do established academic and scientific publishing systems (journal publishers, preprint servers, living review platforms) handle post-publication corrections, amendments, retractions, and formal c…
ServiceNow workflow orchestration and agentic Artificial Intelligence (AI)…
What workflow orchestration and governance capabilities does ServiceNow currently provide for Artificial Intelligence (AI) agent workloads, specifically its identity resolution, permissions, audit tra…
Governance-as-moat thesis and prior research implications
How does the thesis advanced in the April 2026 Liam Hyland and Leonis Capital ServiceNow analysis, that governance is the durable, non-replicable value layer in AI-augmented enterprise technology stac…
Enterprise data stack value-distribution frameworks
What frameworks - specifically the seven-layer enterprise stack and the Software Repricing Matrix described in the April 2026 Liam Hyland ServiceNow analysis video, together with comparable frameworks…
Universal Entity Lifecycle Governance Framework (UELGF)
What is the complete specification of the Universal Entity Lifecycle Governance Framework (UELGF), integrating foundational definitions and principles, entity taxonomy and Confidentiality, Integrity,…
Universal Entity Lifecycle Governance Framework (UELGF)
How should the UELGF specify the runtime feedback loop, covering signal taxonomy, signal aggregation and evaluation mechanism, automated response taxonomy proportionate to signal severity, re-evaluati…
Universal Entity Lifecycle Governance Framework (UELGF)
What policy architecture, covering Policy Administration Point (PAP), Policy Decision Point (PDP), Policy Enforcement Point (PEP), and Policy Information Point (PIP), and what 8-layer organisational c…
Universal Entity Lifecycle Governance Framework (UELGF)
How should the UELGF specify governed golden rails for each entity type and Confidentiality, Integrity, and Availability (CIA) tier such that the rail is generative, with a complete governed scaffold…
Universal Entity Lifecycle Governance Framework (UELGF)
What are the foundational definitions, formal principles, and architectural properties required to specify the Universal Entity Lifecycle Governance Framework (UELGF) such that it applies consistently…
Universal Entity Lifecycle Governance Framework (UELGF)
What canonical entity taxonomy and Confidentiality, Integrity, and Availability (CIA) classification system should the UELGF use to determine governance intensity, ensuring that every entity type, fro…
Universal Entity Lifecycle Governance Framework (UELGF)
How should the UELGF formally specify the decommission lifecycle, including a complete trigger taxonomy, procedural requirements differentiated by CIA tier, a ghost-entity detection and remediation me…
Invariant-based anomaly detection in the Policy Information Point (PIP)
How can the Policy Information Point (PIP) detect when a governed asset's transient operating context is being used, intentionally or through task creep, to suppress or obscure a permanent invariant,…
Universal policy synchronisation and integrity
What mechanism ensures that the Policy Decision Point (PDP) evaluates a governed asset against logically identical policy at every lifecycle phase, such that a soft gate in Development and a hard gate…
Policy Administration Point (PAP) dynamic policy profiling and proportionality
How can a Policy Administration Point (PAP) dynamically map a governed asset's metadata, specifically its invariants and Confidentiality, Integrity, and Availability (CIA) ratings, to a proportional a…
Out-of-band policy invalidation and remediation
What consistency model governs [Policy Administration Point (PAP)](https://docs.oasis-open.org/xacml/3.0/xacml-3.0-core-spec-os-en.html)-to-[Policy Enforcement Point (PEP)](https://docs.oasis-open.org…
Cryptographic preservation and runtime evaluation of original intent
What representation of original intent, captured at the Getting Started phase, is simultaneously cryptographically verifiable and semantically stable enough to function as a meaningful evaluation base…
What is the strongest evidence-based argument that investing in software…
What is the strongest evidence-based argument - drawing on Yann LeCun's primary sources, the formal methods literature, the systems capability debt research already in this corpus, and empirical evide…
What is the precise technical distinction between code generation and other…
What is the precise technical distinction between code generation and other Large Language Model (LLM)-generated outputs in terms of external verifiability, specifically, that code operates in a forma…
What is Yann LeCun's complete argument against Large Language Models as a path…
What is Yann LeCun's complete and precise argument against Large Language Models (LLMs) as a path to autonomous machine intelligence, meaning Artificial Intelligence (AI) that can reason, plan, and ac…
What does synthesising LeCun's architectural critique of Large Language Models…
What does the synthesis of Yann LeCun's architectural critique of Large Language Models (LLMs), no causal world model, no consequence reasoning, verifiable only in formal systems, with the systems cap…
What constraints do vendor platforms impose on governance, and how should…
What governance constraints are imposed by major vendor Artificial Intelligence (AI) and low-code platforms, specifically, what governance capabilities are natively supported versus where external con…
When and how should human intervention be incorporated into Artificial…
When and how should human intervention be incorporated into AI-driven and automated workflows, specifically, what trigger conditions, intervention thresholds, escalation procedures, response time expe…
How can enterprise data governance frameworks be consistently enforced within…
How can enterprise data governance frameworks be consistently enforced within Artificial Intelligence (AI) and visual, minimal-code application environments, specifically, how should data classificati…
How should AI and low-code governance integrate with existing software…
How should Artificial Intelligence (AI) and low-code governance integrate with existing software development and platform engineering practices, specifically, how should governance controls be integra…
How should Artificial Intelligence (AI) and low-code use cases be classified…
What structured risk classification framework is appropriate for AI and low-code use cases in enterprise environments, specifically, how should categories such as informational, decision-support, and…
How can enterprise Artificial Intelligence (AI) and low-code governance…
How can enterprise Artificial Intelligence (AI) and low-code governance frameworks be aligned with external regulatory and compliance obligations, specifically, what is the mapping between governance…
What observability and telemetry model is required to govern Artificial…
What observability and telemetry model is required to govern AI and low-code systems at scale, specifically, what must be logged, at what frequency, and at what level of granularity, including prompt…
What lifecycle management model is required for Artificial Intelligence (AI)…
What comprehensive lifecycle management model is required for AI models, prompts, and low-code applications, covering versioning strategies, deployment controls, rollback mechanisms, ownership trackin…
What maturity model best describes the evolution of governance capabilities for…
What maturity model best describes the evolution of governance capabilities for AI and low-code in enterprises, specifically, what are the clearly defined maturity stages, capability benchmarks, and p…
Where should governance enforcement points be implemented within enterprise…
Where should governance enforcement points be implemented within enterprise architecture for Artificial Intelligence (AI) and low-code systems, specifically, at which architectural layers (Application…
What are the primary failure modes in enterprise Artificial Intelligence (AI)…
What are the primary failure modes in enterprise Artificial Intelligence (AI) and low-code deployments, including data leakage, conflicting automations, unintended actions by AI agents, and loss of au…
How should decision rights, accountability, and liability be structured for…
How should decision rights, accountability, and liability be structured for AI systems and low-code applications in enterprise environments, specifically, who should be empowered to approve new use ca…
How do organisational incentives, culture, and behaviour influence adherence to…
How do organisational incentives, culture, and behaviour influence adherence to governance in Artificial Intelligence (AI) and low-code environments, specifically, what conditions drive teams to bypas…
What is the cost, performance, and delivery impact of governance controls on AI…
What is the cost, performance, and delivery impact of governance controls on AI and low-code development, specifically, what economic model quantifies the trade-offs between governance strength and de…
What identity and access management model is required for Artificial…
What identity and access management (IAM) model is required for non-human actors, AI agents and low-code artefacts, operating within enterprise systems, specifically: how should machine identities be…
What control-plane architecture is required to manage Artificial Intelligence…
What control-plane architecture is required to manage AI agents and low-code systems as distributed, semi-autonomous actors within enterprise environments, specifically, how should policies be created…
Implicit rate-limiting controls removed by agentic Artificial Intelligence (AI)
Prior to agentic Artificial Intelligence (AI), the blast radius of ungoverned citizen development was implicitly bounded by human speed, attention, fatigue, and working hours, controls that are not do…
Deployment pipeline as the only enforceable control gate for citizen-developed…
In an environment where citizen development tooling is already licensed and accessible to non-technical staff, and where the distinction between personal productivity and production automation has col…
Access control amplification under agentic operations
Agents do not inherit a user's typical behaviour, they inherit the worst-case interpretation of that user's full permission set, because they operate without fatigue, attention limits, or working hour…
Policy coherence as a machine-checkable prerequisite
Contradictory or outdated policy documents are a chronic governance failure that organisations tolerate because the consequences under human operation are slow-moving. Under agentic operation, agents…
Permission-safe Retrieval-Augmented Generation (RAG) in enterprise information…
What are the technical constraints on permission-safe Retrieval-Augmented Generation (RAG) in an enterprise information architecture with incoherent access controls, collaboration groups created ad ho…
Dependency ordering of foundational conditions for safe agentic Artificial…
The foundational conditions for safe agentic AI deployment in a regulated financial institution are not independent, they form a dependency graph in which policy coherence is a prerequisite for inform…
Systems capability debt as the root cause of citizen development
What empirical evidence exists that systems capability debt, the accumulated gap between what people need from their systems and what those systems deliver across integration, functionality, data acce…
Systems capability debt, citizen development, and agentic AI risk
Does the synthesis of technical debt literature (Cunningham, Kruchten), systems capability research, transaction cost economics (Coase, Williamson), operational risk frameworks (Basel III/IV, Risk and…
Regulatory and standards preconditions for deployment of Artificial…
Under applicable regulatory and standards frameworks, including Australian Prudential Regulation Authority (APRA) CPS 230, the European Union (EU) Digital Operational Resilience Act (DORA), Payment Ca…
Multi-provider AI control planes
Which platforms or architectural designs provide multi-provider Artificial Intelligence (AI) control planes that unify discoverability, oversight, logging, security, data-access control, Financial Ope…
What is Microsoft 365 Copilot Cowork and what are its enterprise governance…
What is Microsoft 365 (M365) Copilot Cowork, how does it technically differ from custom Microsoft Copilot Skills, and what are the governance, legal, and shadow Information Technology (IT) risks it in…
Global artificial intelligence agent regulation in financial services
What regulatory obligations do financial-services regulators globally, including the European Union (EU), Australia, New Zealand (NZ), the United States (US), and the United Kingdom (UK), impose on Ar…
Business-led low-code agent governance
Under what conditions does business-led low-code Artificial Intelligence (AI) agent creation produce durable organisational value versus technical debt and governance fragmentation, and what foundatio…
Historical technology adoption patterns as analogues for enterprise Artificial…
What can organisations learn from retrospectives of prior technology introductions, specifically personal computing, Enterprise Resource Planning (ERP), cloud computing, Robotic Process Automation (RP…
Knowledge curation governance as an enterprise AI capability in regulated…
What operational models exist for governing authoritative knowledge as a managed enterprise capability for Artificial Intelligence (AI) consumption in regulated financial institutions, covering domain…
Enterprise AI use-case routing frameworks
What decision frameworks do enterprises use to route Artificial Intelligence (AI) use cases to the appropriate platform, implementation pattern, and risk tier, distinguishing low-code business-led, pr…
Enterprise AI platform operating models
What organisational structures do enterprises use to operate multiple Artificial Intelligence (AI) platforms simultaneously, and what trade-offs emerge between (a) a single unified AI platform team, (…
Automated governance assurance and change control verification patterns for…
What technical patterns exist for automating governance assurance and change control verification in Artificial Intelligence (AI)-assisted delivery pipelines, specifically audit evidence generation, p…
Recall competitive landscape and clone feasibility
What core capabilities does Recall provide, who else is building similar products (including projects in the davidamitchell GitHub organization and relevant open-source tools), what components can we…
Enterprise AI capability model for use-case maturity decisions
What enterprise-wide Artificial Intelligence (AI) capability model best supports deciding whether a candidate AI use case requires net-new foundational capabilities or can reuse capabilities already b…
Harness-level selection and use of tools, agents, skills, prompts, and…
When should teams choose tools, agent definition files, skills, prompts, instruction files, and AGENTS.md, and what verifiable best practices align with how major harnesses actually select and apply e…
Latest developments history
What trends, themes, and directional shifts are visible in the source material at `Latest-developments-/history` and related public sources, and what are the most plausible evidence-grounded speculati…
Artificial Intelligence (AI)-assisted daily productivity digest
What are the established patterns and tooling approaches for using Artificial Intelligence (AI) to generate actionable daily and weekly productivity digests from personal task management systems?
Sup…
Claude mythos: character, soul documents, and narrative identity in large…
What is the "Claude mythos" - the narrative, character, and values framework Anthropic has built into Claude - and who else in the industry is doing similar work on giving large language models (LLMs)…
AI company hiring strategies
What do recent and historical job advertisements and hiring patterns at major Artificial Intelligence (AI) companies signal about their current and emerging strategic priorities, and where are the mos…
Shopify's Artificial Intelligence (AI) strategy after the Red Queen memo
What is Shopify's explicit Artificial Intelligence (AI) strategy as evidenced by Toby Lütke's "prove AI cannot do it before you hire" memo and follow-on operating decisions, and how has that strategy…
The shape of organisations when software is no longer the constraint
Inside an organisation that requires software to be built, integrated, and maintained (including Commercial Off-The-Shelf (COTS) systems, Software-as-a-Service (SaaS) platforms, and bespoke-built syst…
oh-my-codex and AI Agent Workflow Patterns
What patterns from oh-my-codex (OMX) and similar AI agent workflow projects (AGENTS.md, SKILL.md, etc.) are most applicable to improving the instructions, skills, agents, and tooling across davidamitc…
Anthropic Claude Code leak
What does the accidental March 2026 leak of Anthropic's Claude Code source code reveal about: (1) the codebase architecture, (2) how key engineering problems are solved, (3) the prompting and instruct…
Claude Code npm Source Map Leak
How did the March 2026 accidental leak of Anthropic's Claude Code source code via an npm (Node Package Manager) package occur, and what processes and protections can organisations adopt to prevent sim…
AI Funding and Capital Investment Landscape
Which Artificial Intelligence (AI)-related and tech companies are receiving and deploying capital investment in 2023-2026, who the major investors are, where investment is concentrated, and what actio…
Backpressure Infrastructure and the Theory of Constraints
What is backpressure infrastructure, specifically as it pertains to the Theory of Constraints (TOC), and what does academic research and real-world white papers say about its practical application?
TimesFM and the Landscape of Time-Series Foundation Models
What are the practical use cases for TimesFM (Google's pretrained time-series foundation model), who is doing comparable work, and how does the foundation-model paradigm extend to other structured dat…
Large Language Models as offensive security tools
What is the current state of Large Language Model (LLM)-driven offensive security capability: can LLMs autonomously discover and exploit zero-day (0-day) vulnerabilities, what does the empirical evide…
The Unknowability of the Universe
What does JB Manchak's treatment of General Relativity (GR) and Zen Buddhism reveal about the epistemological limits of human knowledge: specifically, is the universe fundamentally unknowable, and if…
The role of AGENTS.md in a repo using .github/copilot-instructions.md as the…
`AGENTS.md` has emerged as the cross-tool convergence format for agent project instructions, supported by OpenAI Codex, GitHub Copilot, Claude Code, Cursor, Aider, Gemini Command Line Interface (CLI),…
Multi-agent repo setup
What are the best practices for setting up a GitHub repository so that it can be worked on effectively by multiple Artificial Intelligence (AI) agents, specifically: (1) Claude via the iOS Claude app,…
Claude Code on the web
Does Claude Code on the web automatically initialise git submodules when cloning a repository, and if so, can it access private submodules (such as `davidamitchell/Skills` referenced at `.github/skill…
Environment setup consistency
Given the two primary agent entry points, (A) assigning a GitHub issue to the Copilot coding agent and (B) using the Claude iOS `code` feature, what environment does each agent start in, and what cont…
Agent instruction loading and skills access
Given the current repo setup -- instructions in `.github/copilot-instructions.md`, skills submodule at `.github/skills/`, no `AGENTS.md` at root, no `CLAUDE.md` at root -- what does each agent actuall…
Rory Sutherland's core tenets
What are Rory Sutherland's core intellectual tenets, particularly around anti-bureaucracy, customer thinking, and behavioral economics, and what practical implications do they hold for business strate…
The measurement asymmetry
Why is measuring opportunity cost systematically harder than measuring direct costs, and what cognitive and structural mechanisms cause organisations to destroy value while claiming efficiency gains?
Customer contact as strategic signal
When customers contact a business, what are they actually seeking — and how should organisations decide between self-service automation and human interaction to maximise long-term customer value?
Cost reduction is not a strategy
Why is cost reduction insufficient as a business strategy, and how does framing artificial intelligence (AI) primarily as a cost-cutting tool risk destroying value through missed opportunities?
Against bureaucracy: dismantling control systems to focus on value and…
What does the synthesis of the Anti-Bureaucracy Manifesto and James Burnham's *The Managerial Revolution* reveal about how organisations can dismantle control systems and system waste while refocusing…
Bureaucracy growth and the boomer generation hypothesis
Who has written or researched the idea that the growth of bureaucratic functions — specifically Human Resources (HR), Finance, and Procurement — was led or significantly amplified by the baby boomer g…
Public sentiment on AI in banking and high-trust institutions
What does current (2024–2025) survey data reveal about customer sentiment toward Artificial Intelligence (AI) in banking and high-trust Financial Services (FS) institutions — in Australia, across Asia…
The Software Factory
If the cost of producing high-quality, standardised, integrated software is approaching zero — as Artificial Intelligence (AI) coding agents and software factory patterns suggest — what must organisat…
Agent orchestration patterns
What agent orchestration patterns, verification strategies, and multi-model delegation techniques are demonstrated by Burke Holland's Anvil, Max, and the orchestrator/planner/coder/designer multi-agen…
Artificial Intelligence (AI) agents as finishers and synthesisers
What agent configurations, prompt strategies, orchestration patterns, and tooling choices allow an AI agent (or agent team) to act as a reliable *finisher* and *synthesiser* - completing work that a h…
Are Human Brains Just Prediction Machines? Comparing Predictive Processing and…
What is the fundamental difference between the predictive processing account of human cognition — in which the brain continuously generates and updates a generative model of the world — and Large Lang…
How to best use awesome-copilot in this repo and across personal repos
What resources from `davidamitchell/awesome-copilot` — GitHub Copilot (GHC) instructions, skills, agents, workflows, hooks, and plugins — provide the most leverage when applied to this Research repo a…
Code Architecture Inspection Across Repositories
What practical implementation approaches exist for automatically inspecting and understanding how a set of repositories is architected, how they relate to and couple with each other, and whether they…
Tracking How Work Travels Across Organisational Systems
Can we track how a unit of 'Work' -- an idea or concept -- travels across organisational systems (SharePoint, Confluence, Azure DevOps (ADO)/Jira, Git, monitoring systems, and data platforms), and wha…
Working memory architecture, prefrontal cortex contextual gating, and…
How do human brains store, compress, retrieve, and dynamically layer multiple types of contextual knowledge — values, goals, rules, current state, and immediate task — when making decisions, and what…
Cross-Scanner Compliance Evidence and Waiver Normalisation in GitHub Actions
How should an organisation running multiple compliance scanners in GitHub Actions normalise evidence, severity, waiver handling, and developer-facing output so that heterogeneous tools behave like one…
Compliance Scanning via GitHub Actions — Broad Policy as Code Across a…
How can GitHub Actions (with GitHub Advanced Security (GHAS) and CodeQL already enabled) be extended to enforce a broad, organisation-wide compliance policy — covering naming conventions, architectura…
Applied context engineering
What practical patterns, workflow best practices, and agent development guidelines emerge from synthesising the `muratcankoylan/Agent-Skills-for-Context-Engineering` skill library with the context eng…
Coding AI Agent Skills Survey
What actively maintained, publicly available agent skills, prompt libraries, instructions files, and system prompts exist — from vendors such as Microsoft and from the Open Source Software (OSS) commu…
Technology Capability Models
What established and emerging IT capability models define a complete, multi-level set of technical capabilities - such as authentication, networking, Application Programming Interface (API) gateways,…
Dependency Mapping Across .NET Codebases, Terraform, Dynatrace, Confluence, Log…
What practical tools and methodologies are being used to map dependencies across .NET codebases, Terraform configurations, Dynatrace Application Performance Management (APM) monitoring, and solution d…
Layered Organisation Large Language Model
Is it technically feasible and economically viable for an organisation to build a customised Large Language Model (LLM) layer that injects and optimises over organisation-specific context - internal k…
More formal proof engineering
What does Leanstral - an open-source agent for formal proof engineering - offer as a practical path to trustworthy, formally verified software built with Artificial Intelligence (AI) assistance, and h…
Explore to exploit: the synthesis step that makes exploitation pay off
When technology is moving fast, what is the synthesis step between exploration and exploitation, why is it so commonly skipped, what are the costs of skipping it, and what strategies allow organisatio…
Application Programming Interface (API) Context Hubs, Retrieval-Augmented…
What approaches are being used to enable Artificial Intelligence (AI) agents to discover, understand, and invoke external Application Programming Interfaces (APIs), and how do the three major emerging…
Stateless-agent assumption failure
When an agentic workflow spans multiple session boundaries — each session starting with a fresh context window and no memory of prior runs — what are the mechanisms by which external state becomes orp…
Artificial Intelligence (AI) Memory Systems
What is the current state of Artificial Intelligence (AI) memory systems — across Retrieval-Augmented Generation (RAG) research, commercial AI vendor implementations (GitHub Copilot, Gemini, Claude, a…
Vision-Language Joint Embedding Predictive Architecture (VL-JEPA) and concept…
What is Vision-Language Joint Embedding Predictive Architecture (VL-JEPA) - specifically its concept prediction mechanism - and what practical options exist for a developer consumer of existing fronti…
Intent Driven Development
What is Intent Driven Development (IDD) — as a methodology that moves past Test Driven Development (TDD) and Specification Driven Development (SDD) — and what context and concept layering mechanisms a…
GitAgent and declarative agent definition
What is GitAgent (https://github.com/open-gitagent/gitagent), how can it be used in this repository, what concepts does it build on and produce, and how does the broader idea of declarative agent defi…
Adaptive Policy-Based Authorization (APBA)
How does Adaptive Policy-Based Authorization (APBA) align with the dynamic access-control requirements of National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53 and ISO/…
Trusting Trust and AI Corpus Contamination
Ken Thompson's "Trusting Trust" argument shows that you cannot verify a compiler by reading its source code if the compiler was compiled by a compromised toolchain — the contamination lives in the bin…
Invariants in Software as a Service (SaaS) Banking Software
What capabilities do enterprise Software as a Service (SaaS) banking platforms (principally Salesforce Financial Services Cloud (FSC) and nCino) provide as true invariants - independent of customer im…
Prompt injection threat landscape
What is the current state of the prompt injection threat in agentic artificial intelligence (AI) systems: who is exploiting it, who is defending against it, and what does the research community consid…
Aligned Decision-Making
What framework should an organisation adopt to ensure that AI agents making or supporting decisions have access to the right layered organisational context — spanning regulatory boundaries, values and…
Latent Concept Extraction from Confluence
What are the best approaches for extracting latent concepts from a Confluence wiki, representing them as word embeddings in a vector database (VDB) and as a knowledge graph (KG), and how can the resul…
Adam Smith, Organisational Design, Desire Paths, and AI Strategy
What can Adam Smith's insights into human nature and morality - drawn from *The Theory of Moral Sentiments* (ToMS) and *The Wealth of Nations* (WoN) - teach us about designing organisations that align…
Reliable Software in the LLM Era
What strategies and formal-methods tooling exist for maintaining software reliability in the Large Language Model (LLM) era, and what does the Quint formal specification language ecosystem - including…
Context Compression and RAG Techniques for Organisational Knowledge
What are the current best practices and bleeding-edge techniques - including Retrieval-Augmented Generation (RAG), context compression, and context architecture - for selectively surfacing the rig…
ChatGPT Actions and custom GPTs
Can a ChatGPT custom Generative Pre-trained Transformer (GPT) be configured with Actions that: (a) call a self-hosted HTTP endpoint to add a memory, (b) call `search_brain` before responding to surfac…
Ricardian Contract model
What is the Ricardian Contract model proposed by Ian Grigg in 1996, how has it evolved over the past three decades, who is actively building with it today, and what does the latest academic and applie…
SWAT technique in a fresh-context loop
When the SWAT (Strengths, Weaknesses, Assumptions, Threats) technique is executed repeatedly in a loop where each invocation uses a fresh Large Language Model (LLM) context window and the caller blind…
Can organisational intent be expressed as a formally structured specification…
Can organisational intent — mission, values, strategy, resource allocation — be expressed as a formally structured specification from which human-readable artefacts are derived, and against which Obje…
Hosting options for the Research repo
What is the best free or very-low-cost hosting option for this research repository that supports full-text search, and optionally vector/graph database capabilities, without requiring SEO, custom DNS,…
Best practices in financial forecasting for IT operational run costs
What are the established best practices for financially responsible forecasting of Information Technology (IT) operational run costs — covering cost estimation by technology and infrastructure type, r…
AI inverted the knowledge-work scarcity equation
Before Artificial Intelligence (AI), throughput (volume of output) was the binding constraint on knowledge work. AI has
dramatically reduced the cost of generating output. Does the evidence support th…
Three disciplines, one answer
Software engineering (Fred Brooks, 1975), evolutionary psychology (Robin Dunbar, 1992), and graph
theory each independently arrive at the same structural limit: approximately 5 people for a
high-coord…
Failure mode taxonomy
The five-layer failure mode taxonomy established in `2026-03-10-ai-concept-classification-taxonomy.md` (Q5) provides a structurally sound classification, but leaves three empirical gaps unanswered: (1…
Exploration-synthesis gap
During periods of rapid exploration — such as the current wave of Artificial Intelligence (AI) / Large Language Model (LLM) adoption inside organisations — individuals and teams routinely duplicate ef…
AI amplified the coordination tax
Artificial Intelligence (AI) has increased per-person output by 5–10x. If coordination cost scales with the square of team size
(see `2026-03-12-team-size-limits-brooks-dunbar-network-theory.md`), wha…
Force multiplier, not cost reducer
When Artificial Intelligence (AI) multiplies the productive capacity of each person by 5–10x, organisations face a
strategic choice: reduce headcount to cut costs, or redeploy the same people against…
Research loop evaluation rubric
What structured rubric should be used to evaluate the outputs of this repository's research loop agent — and what does a minimal viable implementation of a Continuous Integration (CI)-integrated eval…
The Nature of the Firm
Why do organisations (firms and business units) exist when markets are theoretically efficient? What are the fitness functions and invariants that determine when organisational form is the correct coo…
Superpowers as inspiration
What ideas, patterns, and workflow practices from `davidamitchell/superpowers` (a fork of `obra/superpowers`) can be used as inspiration for improving agent tooling in `davidamitchell/Latest-developme…
Language designed for LLM agents to produce
Is anyone actively developing a programming language or structured output format specifically designed for LLM agents to generate — rather than humans to write — that structurally addresses generation…
The DIKW pyramid: transformation functions from data to information to…
What are the transformation functions that move between the levels of the DIKW pyramid — Data → Information → Knowledge → Wisdom? What cognitive, computational, and organisational mechanisms perform e…
Agent evaluation framework
What evaluation framework allows systematic comparison of agent implementations across multiple repositories — identifying what problems each is solving, whether concepts are used idiomatically or in…
Adversarial agents with shared goals
What is the design pattern for a system of agents — human or AI — that share a common goal but deliberately occupy different competency domains and time horizons? How does "adversarial collaboration"…
AI concept classification taxonomy
What is a coherent, internally consistent classification taxonomy for the core concepts in AI-assisted and agentic systems — covering prompt types, instruction types, prompt/content/intent engineering…
YouTube transcripts via third-party transcript APIs (AssemblyAI / Supadata)
Can a third-party transcript API (AssemblyAI, Supadata, Kagi, or similar) retrieve YouTube
transcripts from a GitHub Actions runner, bypassing YouTube's IP-based block on the
internal transcript endpo…
Interface and delivery
Once research is complete and outputs are produced, how should they be surfaced and delivered to the people (or agents) who need them? What interfaces make research outputs most usable?
Formal intent specification and language choice for AI alignment in agentic…
Can formal specification of task intent structurally eliminate reward hacking and intent mismatch in agentic coding systems? What is the expressiveness-verifiability tradeoff at each level of the spec…
Telegram bot as mobile memory capture and retrieval channel
Can a Telegram bot serve as a low-friction mobile capture and retrieval surface for the Memory-System? Specifically: (a) message received → file written to GitHub repo via API, (b) messages starting w…
Slack as a mobile memory capture and retrieval channel
Can a Slack bot in a personal or team workspace serve as a memory capture and retrieval surface? What is the minimum viable setup: slash command vs bot, incoming webhook vs Socket Mode, and does a fre…
Better Business Cases
What is the Better Business Cases (BBC) Five Case Model framework, what are the requirements and standards for each of the five cases, and how should an AI agent apply this framework to author, review…
ServiceNow Process Mapping
What options exist within ServiceNow for documenting, mapping, and maintaining business and IT processes — and which approaches are sustainable enough in practice to stay meaningful, current, and actu…
ServiceNow AI: Knowledge Management, RAG Pipelines, and Agent Frameworks
How is ServiceNow evolving its platform to support AI-powered knowledge management, retrieval-augmented generation (RAG), and agent frameworks — and what should an organisation investing in ServiceNow…
ServiceNow Platform Strategy
Given the findings from the Common Service Data Model (CSDM) data modelling, process mapping, and AI capability research, how should an organisation develop a coherent, practical ServiceNow platform s…
Self-hosted MCP server options
What is the minimum viable self-hosted deployment of `mcp_server.py` (or a write-only HTTP wrapper) that: (a) is reachable from the public internet, (b) has zero or near-zero ongoing cost, (c) require…
Context engineering: first principles of steering LLM output without control
What are the first principles of context engineering — and what novel approaches emerge when it is understood as two distinct but coupled mechanisms: (1) making the next predicted token more likely to…
AI coding harnesses: agent execution model, memory, and context management…
What are the core architectural and philosophical principles behind the AI coding harnesses (agentic IDEs and agent runtimes) published or released by Anthropic, OpenAI, and the broader ecosystem of c…
Emergent Patterns in Software Engineering Prompts and SDLC Guidance
What are the current and emergent best practices for crafting AI agent prompts and tooling guidance tailored to each phase of the Software Development Life Cycle (SDLC) — covering discovery, requireme…
LanceDB index rebuild speed from git
Can the LanceDB index be rebuilt from the `.md` files in the repo on startup fast enough to enable stateless (per-request) deployment? Measure rebuild time at: current corpus size, 100 files, 500 file…
iOS Shortcuts + GitHub API
Can an iOS Shortcut write a timestamped `.md` file directly to a GitHub repo via the Contents API (`PUT /repos/{owner}/{repo}/contents/{path}`) with a stored Personal Access Token (PAT), with enough r…
Inbox folder pattern
Does removing the folder-selection decision from the capture path meaningfully reduce friction? Design and evaluate an `inbox/` folder pattern where: (a) any capture tool writes unstructured notes to…
Claude for iOS: MCP remote integration for memory capture and retrieval
Does the Claude iOS app support MCP connections to a remote server? If so: (a) what transport is supported (HTTP/SSE vs stdio), (b) does it require the same `.mcp.json` config as Claude Desktop, (c) w…
ServiceNow CSDM: Practical Data Modelling Across ITSM, APM, SPM, IRM, and FSO
How should organisations model their enterprise data in ServiceNow to meet the CSDM standard while keeping the model maintainable and accurate — and what are the practical patterns for aligning IT Ser…
How organisations practically implement IT RUN vs BUILD cost allocation
How have organisations actually implemented a working RUN vs BUILD IT cost allocation — specifically: how did they agree on what counts as an "application", how did they get consistent work-item taggi…
Slack and MS Teams integration for research delivery and capture
What is the most practical way to integrate the research corpus with Slack and/or Microsoft Teams — for both outbound delivery (notifying when new research is completed) and inbound capture (receiving…
Semantic and full-text search over the research corpus
What combination of full-text search (keyword/BM25) and semantic search (embeddings/vector) is most appropriate for querying the `Research/completed/` corpus, given the constraints of a git-based, loc…
RUN vs BUILD IT spending allocation in non-IT primary businesses
How do non-IT primary businesses (manufacturing, retail, finance) calculate and apportion IT spending between RUN (nondiscretionary operational sustainment) and BUILD (discretionary strategic enhancem…
Coverage gaps in automated research review skills, peer review patterns for…
What review methodology is required to reliably move from information gathering through to applied knowledge and wisdom — and which of those steps can be automated in a CI pipeline versus requiring hu…
iOS Shortcuts for research capture and query
What iOS Shortcuts workflows provide the most value for a personal research system hosted on GitHub — covering both low-friction research capture (adding a URL or idea to the backlog from anywhere on…
An Integrative Framework for Agent Decision-Making
How can the DIKW (Data → Information → Knowledge → Wisdom) progression be operationalised within agentic systems to produce intent-aligned, context-aware decisions that reconcile conflicting knowledge…
Conversational and chat interface for querying the research corpus
What is the best approach to expose the `Research/completed/` corpus as a queryable, conversational interface — so that a user (or an AI agent) can ask "what do I know about X?" and receive a grounded…
AI capability is not a data problem - why the data/analytics department is the…
What is the strongest case - technical, architectural, organisational, legal, and regulatory - that an organisation's AI capability should NOT be owned by or coupled to its data/analytics department o…
Guiding Headless Agents via LSP-Like Mechanisms for Org Policy Conformance
Who is building solutions that allow headless autonomous coding agents to be guided in real time by LSP-like mechanisms — rather than CI gates or pre-commit hooks — to conform to an organisation's sec…
YouTube transcripts via yt-dlp audio + Whisper transcription
Can we bypass YouTube's IP-based transcript block by downloading the **audio track** with
`yt-dlp` (a different endpoint from the transcript API) and then transcribing it with
OpenAI Whisper?
YouTube transcripts via Gemini API (native YouTube URL support)
Can we use the Gemini API (already configured in `davidamitchell/Latest-developments-`) to
extract full transcripts from YouTube videos without being blocked by YouTube's IP restrictions?
RBNZ AI Supervisory Expectations
What are the Reserve Bank of New Zealand's specific supervisory expectations for AI use by regulated entities, and how do these align with or diverge from the expectations of comparator regulators (AP…
Interoception and the predictive self
What is the evidence that the sense of self emerges from interoceptive predictive processing — and what are the implications for understanding depersonalisation, emotion, and mental illness?
Pre-Training Origins of Hallucination-Associated Neurons — Implications for LLM…
Given that Hallucination-Associated Neurons (H-Neurons) emerge during pre-training rather than instruction tuning or RLHF, what does this reveal about how hallucination-prone behaviour is encoded duri…
Over-Compliance in LLMs — How H-Neurons Drive Sycophancy and What Interventions…
What exactly is over-compliance behaviour in LLMs, how do Hallucination-Associated Neurons (H-Neurons) cause it, and what neuron-level and inference-time interventions are feasible to reduce it withou…
H-Neurons Synthesis — From Hallucination Mechanisms to Actionable LLM…
Across all four preceding research items — the macroscopic hallucination landscape, the H-Neurons paper, over-compliance interventions, and pre-training origins — what is the unified, actionable pictu…
Swarm Intelligence, PCA, Genetic Algorithms, and Reinforcement Learning —…
What is the structured, decision-oriented landscape of four advanced technique families — Swarm Intelligence, Principal Component Analysis (PCA) and its modern extensions, Genetic Algorithms and Evolu…
Self-improving Artificial Intelligence (AI) agent evaluation loop architecture
What is the most principled architecture for a Self-Improving AI Agent Evaluation Loop — specifically, how should a nested inner/outer loop be designed so that a "Meta-Optimizer" rewrites system promp…
Hallucination-Associated Neurons (H-Neurons) in LLMs — Identification,…
What are Hallucination-Associated Neurons (H-Neurons) in large language models, how can they be identified, what behaviours do they cause, where do they come from, and what do these findings imply for…
LLM Hallucinations — Types, Causes, and Current Mitigation Approaches
What are the established types, root causes, and current mitigation strategies for hallucinations in large language models, and what does the macroscopic (training-level) view leave unexplained that m…
The hard problem vs. the real problem of consciousness
What is the difference between Chalmers' "hard problem" and Seth's "real problem" of consciousness — is the real-problem strategy a genuine advance or a deferrment of the original question?
Free energy, entropy, and life
Why do living organisms need predictive brains? What is the precise relationship between the thermodynamic concept of entropy (disorder), the information-theoretic concept of free energy (surprise), K…
Exploit versus explore Artificial Intelligence (AI) investment classification
How should organisations distinguish between exploitation and exploration AI investments in practice, and what diagnostic criteria and portfolio tools enable that distinction to be applied at budget a…
Controlled hallucination
What is the evidence that perception is a generative, top-down process rather than a bottom-up readout of the world — and what are the strongest objections to Seth's "controlled hallucination" framing…
Artificial Intelligence (AI) coding assistant deployment outcomes
Which organisations have published or disclosed coherent AI strategies specifically targeting software engineering, what outcomes have they measured, and what does the trajectory from AI-assisted codi…
Artificial Intelligence (AI) security strategy
Which organisations have developed coherent AI strategies with security as the primary objective — either using AI to enhance security posture or governing the security risks that AI systems themselve…
Sources of research: what to monitor and how
What are the best sources for AI/ML research, and what is the right monitoring strategy for each — RSS, YouTube channels, arXiv, newsletters, GitHub?
Machine Learning (ML) technique taxonomy and selection criteria for analytics…
What is the complete, structured landscape of machine learning techniques and algorithms that an advanced analytics department should know, use, and actively pursue — covering foundational concepts, w…
Research output types
What are the possible output types from a research item, and how should each type be handled, stored, and acted upon?
Local index vs reference
For each type of research content, should we store a local copy / index, or just maintain a reference (URL, citation)? What are the right trade-offs between storage cost, offline access, durability, a…
Local database: requirements and technology choice
If we decide to use a local database for indexing and state (rather than JSON files), what are the requirements and what technology should we choose?
Evaluating and improving autonomous research loop quality
How can the quality of research items produced by the `research-loop.yml` autonomous pipeline be systematically evaluated, and what changes to `research-prompt.md` and the loop's prompting strategy wo…
Research agenda curation
How should the research backlog be maintained and prioritised to ensure balanced coverage of important domains, detect over-concentration in one area (research drift), and surface high-value gaps — ra…
Knowledge retention: mechanisms for ensuring completed research is recalled and…
What mechanisms ensure that knowledge from completed research items is retained, recalled when contextually relevant, and applied to decisions — rather than being archived indefinitely with no re-enga…
Knowledge Representation for Agent Context
What techniques — latent semantic extraction, knowledge graphs, concept maps, hierarchical document compression, and layered abstraction — most effectively represent and compress large knowledge corpo…
Knowledge linking: building a connected research corpus via explicit…
What is the minimum viable approach to making the `Research/completed/` corpus a connected knowledge network — where items explicitly reference related items, contradictions and confirmations are surf…
Artificial Intelligence (AI) risk-reduction deployments in financial services
Which organisations have developed AI strategies explicitly framed around risk reduction — operational risk, credit risk, fraud, compliance, model risk — and what governance structures, outcome metric…
Enterprise Artificial Intelligence (AI) efficiency programme outcomes
Which published AI strategies — corporate, national, or sector-specific — are explicitly designed around business efficiency as the primary objective, what measurable outcomes have they produced, and…
Artificial Intelligence (AI) agents in financial services line 1 and line 2…
Who is currently building or deploying AI agents specifically positioned to operate within the three lines of defence model — line 1 (business/operational risk management) and line 2 (risk and complia…
AI for Control Testing, Gap Identification, and Policies/Standards Reviews
Which organisations are using AI to automate control testing, identify control gaps, or conduct policies and standards reviews — and what does the current vendor, practitioner, and regulatory landscap…
YouTube transcript fetcher for research
Can we port the YouTube transcript fetcher from `davidamitchell/Latest-developments-` to this repo and adapt it for research use (bulk fetch, save transcripts, not just email digest)?
Transaction Cost Economics
What are the foundational concepts of transaction cost economics (Coase → Williamson → North → Ostrom), and how might the analytical framework map onto software engineering organisation, AI agent desi…
Agent Memory Management and Context Injection
What is the current state of agent memory management systems — beyond RAG — and which approaches best address the real constraints of latency, knowledge freshness, scoping, governance, quality, and di…
GitHub wiki for research content
What is the best approach for publishing completed research items from `Research/completed/` into the GitHub wiki, and what tooling is needed to keep it current and readable?
Simple process for adding a research item
What is the minimum-friction workflow for adding a new research item so that good ideas get captured before they are lost?
Keeping research backlog separate from repo improvement backlog
What is the cleanest way to separate two distinct types of work — *what to research* vs *how to improve this repo* — so that neither overwhelms the other and each can be prioritised independently?
Information synthesis
What is the best way to synthesise information from multiple sources in a manner that is minimally lossy — preserving the most signal while compressing volume — drawing on information theory, entropy,…
GitHub Specify, Ralph Loops, and Lisa Planning
What is "Specify" in the context of GitHub-integrated AI development workflows, how does the Ralph loop implement proof-driven development in practice, and what role does Lisa planning play in the spe…
Context Mode: MCP tool output compression and the LLM context window management…
What is Context Mode's architecture and actual effectiveness for compressing MCP tool outputs in Claude Code, what are its real-world limitations (especially regarding MCP tool interception), and what…
AI Strategy: global and NZ examples, policy frameworks, regulations, and…
What do leading global AI strategies look like, how does New Zealand's regulatory and policy landscape (RBNZ, DIA, MBIE, and others) compare, and what use-case typology — from human augmentation throu…
Predictive processing and active inference
What exactly is the predictive processing / active inference framework, how does it differ from classical feedforward models of perception, and what is the empirical status of the free energy principl…
Jevons Paradox: efficiency gains, demand rebound, and the falling cost of…
How does Jevons Paradox operate across different sectors historically, what are the conditions under which cost or efficiency improvements *do not* increase total demand, and what do current thinkers…
Indexing and tracking method for research content
What is the best method for indexing and tracking research content (transcripts, papers, notes) given the constraints of a git-based, local-first repo?
Reality Is A Controlled Hallucination — Anil Seth (Essentia Foundation)
What are the key concepts presented in Anil Seth's "Reality Is A Controlled Hallucination" (https://youtu.be/HYUoS0GkGCs), and how do they relate to and reinforce each other when synthesised?
Friction-Aligned Apprenticeshipsynthesis
When organisations deploy AI (Artificial Intelligence) tools as the default channel for policy clarification, employees predictably choose the pathway with the lowest interpersonal and search cost — t…
Human-AI Cognitive Divergence Risksynthesis
Across the completed items on interoception, automation bias, sycophancy, Barnum language, scaled Human-in-the-Loop (HITL) review, situational awareness, skill decay, and desire paths, what common hum…
Systems Capability Debt Thesis Evidence Assessmentsynthesis
Across the completed research items mapped to the authoritative systems capability debt thesis, which thesis claims are supported, qualified, contradicted, or still unresolved, and what is the stronge…
Organisational failure modes from structural fragmentationsynthesis
Across the five completed organisational failure-mode items from 2026-05-14, what common structural mechanism explains why accountability, demand governance, team boundaries, and vendor control repeat…
Regulated enterprise Artificial Intelligence delivery constraint shiftsynthesis
Across the completed research on regulated financial-services obligations, governance economics, hybrid control architectures, verifier-gated software engineering, and software-demand shifts in the La…
Enterprise capability stack for sustainable multi-provider Artificial…synthesis
Across the completed research on enterprise platform operating models, multi-provider control planes, low-code governance, knowledge curation, and coding-agent practice, what socio-technical capabilit…
Enterprise AI use-case classification schemasynthesis
Across the routing, risk-tier, capability, and low-code governance items, what classification model best explains how an enterprise should sort Artificial Intelligence (AI) use cases so it can choose…