Axis Bridge Solutions
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Partially implemented Maturity: intermediate

AI Observability & Evaluation

Trace ledgers, audit rows, and always-on session recording — with formal LLM evaluation as the honest gap in progress.

Executive summary

You cannot improve what you cannot see. Axis Bridge systems log what the model was asked, what it answered, and what the agent did — down to continuously recorded desktop footage indexed by agent run. Formal LLM-output evaluation suites are the explicitly acknowledged gap, and the next investment.

What this capability means

Observability for AI systems has two halves. The first — knowing what happened — is thoroughly built: traces, audits, provenance, and a literal DVR of agent sessions that can replay the minutes before any approval request. The second — knowing whether outputs are good, measurably, over time — is where this portfolio is honestly partial: verification exists as targeted proof batteries and acceptance criteria, not yet as a systematic LLM evaluation practice.

That gap is stated here deliberately. A capability portfolio that only lists strengths is marketing; this one is an operating document, and closing this gap is the current improvement cycle.

Maturity ladder

  1. 01

    Basic — Structured logging

    ● demonstrated

    Every interaction, draft, and action stored with enough context to reconstruct what happened.

    Event → Structured log → Queryable store
  2. 02

    Intermediate — Traces & flight recording

    ● demonstrated

    Per-member prompt/response traces, audit rows on every gated action, and continuous session recording with an intent-event index — a flight recorder for agents.

    Agent run → Action audit + intent events → DVR footage → Run → footage index → Replay
  3. 03

    Advanced — Systematic evaluation

    ○ next

    Golden sets, regression scoring, and faithfulness metrics gating changes to prompts, models, and retrieval.

    Change → Eval suite → Score vs baseline → Gate → Ship or revise

Evidence

  • Trace Ledger exposing the exact prompt sent to each council member and its full response, for every deliberation. llm-council
  • Every workstation action writes an audit row regardless of permission tier; a circuit breaker halts repeatedly-refused runs with a model-written summary. core-workstation permission engine
  • Always-on DVR desktop recording with an intent-events sidecar (action, coordinates, run id — never typed text) and a run-to-footage index with scrub viewer. core-workstation v0.25–0.26
  • Provenance and values-free governance event tables recording every ingest, access denial, and supersession in the knowledge system. me-inc-os-knowledge / db
  • Verification batteries where they matter most — pixel-match proof that the model's visual feed is untouched; RLS isolation proofs; 20/20 acceptance criteria gates. core-workstation tests; opendash verify:rls; ai-education-os PROJECT_STATUS.md