Production Stabilization — Scope & Deliverables

Best fit

• An AI feature or agent system is live and the incident rate is not acceptable.
• Costs or behavior changed and nobody can explain why from data.
• AI-generated code reaches production without a gate you would trust.
• Leadership is asking why "AI" and "reliable" stopped appearing in the same sentence.

What happens, in order

Phase 1 — Audit

• Inventory of what is live: agents, models, providers, data flows, spend.
• Failure-mode analysis: what breaks, how often, who notices first.
• Observability gap review: what questions the current setup cannot answer.
• Findings ranked by blast radius, in writing.

Phase 2 — Instrument & guard

• Agent run traces: what was asked, done, spent, changed.
• Cost and token telemetry into dashboards your engineers already use.
• Deployment guardrails: gated review, protected main, one-step rollback, cost ceilings.
• Permission boundaries: per-tool, least-privilege credentials.

Phase 3 — Stabilize

• Ranked remediation of the real failure modes from the audit.
• Agent-specific incident runbooks: loops, regressions, runaway spend.
• Verification: the failure modes you hired me for demonstrably contained.

Phase 4 — Transfer

• Runbook and architecture documentation your team owns.
• Working sessions until your engineers operate it without me.
• A defined end state — not a retainer by default.

Deliverables

• Written audit with ranked findings.
• Instrumented agent observability (traces + cost).
• Deployment guardrails installed and exercised (including a rehearsed rollback).
• Remediated top failure modes with verification.
• Runbook, docs, and a team that has operated the result.

Scope & pricing

Scoped by quote after the initial deep dive — the spread is wide because "one flaky feature" and "a platform team drowning" are different engagements. Retainer advisory after transfer is available when wanted; it is not the default, and not required for the transfer to stick.