Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: George Andrikopoulos
• Venue: arXiv: cs.AI
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.19125v1
• PDF: https://arxiv.org/pdf/2608.19125v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The author, a systems engineer, argues that the persistent correction of errors in LLM (Language Model) assistants is an operations problem rather than a tooling problem. They propose seven principles for an operating discipline with an error loop at its core to address the failures they've identified in the LLM stack. Three examples from their own practice are provided to illustrate the mechanism, and suggestions for a measurement framework and lab study are made.

What the Document Says

• The author discusses the issue of errors in LLM assistants and how corrections are often not persisted, leading to the recurrence of errors. They claim this is an operations problem rather than a tooling problem.
• They compare the LLM stack to machines operated by their profession (frozen silicon, firmware, loadable modules, persistent configuration, volatile memory).
• The author identifies failures in the LLM stack related to stochastic generation, probabilistic binding of configuration, and lack of a general-purpose retirement (verification) stage by default.
• They derive from these failures a seven-principle operating discipline with an error loop at its core.
• Three examples from the author's own practice are provided to illustrate the mechanism.
• The author suggests a control that was designed to prevent harm but ended up causing it as an example.
• A measurement framework and lab study are proposed to test the proposed operating discipline.

Key Concepts Mentioned

• LLM assistants
• errors
• operations problem
• tooling problem
• systems engineer
• seven-principle operating discipline
• error loop

Claims Made by the Document

• The persistent correction of errors in LLM assistants is an operations problem rather than a tooling problem.

Evidence Used

• When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this is an operations problem, not a tooling problem: mechanisms for persisting corrections exist and are shipping, but the discipline for governing them -- versioning with provenance, recurrence monitoring, counter-metrics, retirement of stale rules -- does not. Writing as a systems engineer of thirty years, I map the LLM stack onto the machines my profession already operates (frozen silicon, firmware, loadable modules, persistent configuration, volatile memory), identify where the mapping fails (stochastic generation, configuration that binds only probabilistically, no general-purpose retirement (verification) stage by default), and derive from the failures a seven-principle operating discipline with an error loop at its core. Three cases from my own practice illustrate the mechanism, among them a control that silently became the exact harm it was built to prevent. I close with the measurement framework this view implies and the lab study required to test it.