Are Multi-Step Large Language Model-Based Systems Inherently Less Explainable…

Are Multi-Step Large Language Model-Based Systems Inherently Less Explainable Than Equivalently Scoped Deterministic Software Systems?

2026-05-18 · agentic-ai ai-architecture governance-policy mlops-deployment explainability-transparency benchmarks-eval · synthesis medium · source → · wiki →

key claims

Explainability in the reviewed literature is multi-dimensional rather than unitary, because formal sources distinguish meaningful explanation, explanation accuracy, transparency, post hoc explanation, and contrastive usefulness instead of treating explainability as one propertyKim (2017)Lipton (2017)Wachter et al. (2018)Phillips et al. (2021)
Present-day Large Language Model systems should be treated as having a local explainability deficit even before scaling, because repeated calls can diverge under fixed settings and exact replay of one decision path is not guaranteedMicrosoft (n.d.)Atil et al. (2025)Denisov-Blanch et al. (2025)
Equivalently scoped deterministic software usually offers better local replayability and clearer bounded causal inspection than a Large Language Model system, because explicit rules, code paths, and logged state can often be rerun and inspected when dependencies are controlledGoogle (n.d.)Humphreys (2009)Hybrid Architecture Design (n.d.)
Production-scale deterministic distributed systems are not globally transparent, because engineers depend on monitoring, white-box telemetry, and post hoc analysis precisely when direct understanding of the whole dependency graph failsGoogle (n.d.)Humphreys (2009)
Useful outcome-level explanation does not always require full internal transparency, because counterfactual explanation can identify what would need to change for a different result without exposing the entire internal mechanismWachter et al. (2018)Phillips et al. (2021)
The explainability gap therefore converges but does not disappear at scale, because deterministic architectures lose global transparency while Large Language Model systems keep additional opacity from learned representations and residual non-determinismLipton (2017)Microsoft (n.d.)Atil et al. (2025)Denisov-Blanch et al. (2025)Humphreys (2009)Google (n.d.)
For governance and audit use cases, the most decision-useful explanation surface in both classes is usually the boundary artefact, logs, rules, traces, approvals, and counterfactual outcome conditions, rather than a full internal causal narrativeWachter et al. (2018)Phillips et al. (2021)Github (n.d.)XAI (n.d.)Governance Policy Application (n.d.)Hybrid Architecture Design (n.d.)

Research Question

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 both classes practically equally opaque?

Findings

Executive Summary

Multi-step Large Language Model-based systems are not equally explainable to equivalently scoped deterministic software systems, because they add model-internal opacity and residual run-to-run variation before production-scale complexity is even considered. Production-scale distributed-system complexity does narrow the gap, because large deterministic systems also become epistemically opaque and depend on monitoring, observability, and post hoc reconstruction rather than direct whole-system inspection. The convergence is therefore partial rather than total: deterministic systems keep an advantage in local replayability and explicit rule-bound explanation, while both classes are weak in global end-to-end explainability at high scale. For governance and audit use cases, the most defensible explanation surface in both classes is usually the boundary artefact, the logs, rules, traces, counterfactual conditions, and approvals that reconstruct the decision, not a claim of full internal transparency.

Key Findings

Explainability in the reviewed literature is multi-dimensional rather than unitary, because formal sources distinguish meaningful explanation, explanation accuracy, transparency, post hoc explanation, and contrastive usefulness instead of treating explainability as one property.
Present-day Large Language Model systems should be treated as having a local explainability deficit even before scaling, because repeated calls can diverge under fixed settings and exact replay of one decision path is not guaranteed.
Equivalently scoped deterministic software usually offers better local replayability and clearer bounded causal inspection than a Large Language Model system, because explicit rules, code paths, and logged state can often be rerun and inspected when dependencies are controlled.
Production-scale deterministic distributed systems are not globally transparent, because engineers depend on monitoring, white-box telemetry, and post hoc analysis precisely when direct understanding of the whole dependency graph fails.
Useful outcome-level explanation does not always require full internal transparency, because counterfactual explanation can identify what would need to change for a different result without exposing the entire internal mechanism.
The explainability gap therefore converges but does not disappear at scale, because deterministic architectures lose global transparency while Large Language Model systems keep additional opacity from learned representations and residual non-determinism.
For governance and audit use cases, the most decision-useful explanation surface in both classes is usually the boundary artefact, logs, rules, traces, approvals, and counterfactual outcome conditions, rather than a full internal causal narrative.

Assumptions

"Equivalently scoped" is treated here as comparable task boundary, side-effect authority, and integration burden rather than identical code size or identical user interface. Justification: the explainability comparison is only meaningful when both systems are asked to do the same class of work at the same governance boundary.
Deterministic software is treated as replayable within a controlled environment, even though production drift, hidden dependencies, and infrastructure changes can weaken that property in practice. Justification: the comparison needs a baseline notion of deterministic execution before asking what scale does to it.

Analysis

The weight of evidence favored formal explainability sources for the definition work and production engineering sources for the classical-system side, because this question turns on both what counts as explanation and what investigators can actually reconstruct in live systems. One rival interpretation is that enough observability investment can erase the gap completely. The reviewed sources do not support that stronger claim, because Google Site Reliability Engineering still treats post hoc reconstruction as an ongoing discipline for complex deterministic systems while Microsoft and repeated-run studies still leave residual LLM variance under stabilization controls. Another rival interpretation is that counterfactual explanation makes internal transparency unnecessary. Wachter supports the practical value of that approach, but NIST and Lipton still distinguish useful explanation from faithful internal transparency, so counterfactuals help with some explanation tasks without solving the full explainability problem. The resulting judgment is therefore a bounded one: classical complexity makes deterministic systems far less transparent than their source code alone suggests, but present-day LLM-based systems still remain structurally worse on local replay and local internal explanation.

Risks, Gaps, and Uncertainties

The accessible Dekker source in this session is the publisher page rather than the full book text, so the item uses it only for bounded systems-theory framing, not for clause-level empirical claims.
The formal explainability literature is richer on machine-learning models than on deterministic distributed software, so part of the cross-class comparison is necessarily inferential rather than explicitly stated in one source.
The strongest practical comparison evidence comes from production engineering documentation rather than from one head-to-head benchmark that measures explainability across both system classes under the same task boundary.

Open Questions

Under what conditions does a deterministic distributed system become so dependency-heavy that its practical local replay advantage also disappears?
Which governance artefacts, traces, rules, approvals, or counterfactual explanations, are most useful to non-specialist reviewers in each system class?
How much could advances in tracing internal model circuits reduce the local explainability deficit for multi-step LLM-based systems without eliminating their residual variance?