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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
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…
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…
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 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 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 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…
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 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 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…
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…
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…
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…
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)
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 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…
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…
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 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…
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…
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…
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…
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…
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…
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, (…
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…
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…
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…
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…
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…
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),…
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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?
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…
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…
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…
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?