ai agent production-readiness audit
Find out where your agents will fail in production — before your users do.
A fixed-scope, two-week audit that measures your agents the way production will: real trajectories, real pass/fail, real cost — and hands you a prioritized, evidence-backed fix list.
Fixed-scope audits · Read-only by default · NDA on request · US/UK/EU remote
What you get
Trajectory evaluation suite
A set of replayable evals over your agents’ real execution paths, so “it works on my machine” becomes a measured pass rate.
False-green-rate report
Where your pipeline reports success it hasn’t earned — the silent failures that pass CI and break in production.
Evidence-binding gap analysis
Every “green” checked against the evidence that should back it; claims that can’t cite their proof are flagged.
Trust-tier & permission review
What your agents are allowed to do vs. what they should be gated on — mapped to an L0–L4 trust model.
Prioritized fix list
Findings ranked by production risk and effort, each with a concrete remediation — not a wall of observations.
Readout call + written report
A live walkthrough plus the sanitized written report you can circulate to your team and stakeholders.
How the audit runs
The same operator protocol every engagement uses — measure first, gate on evidence.
Ground-truth audit first
I instrument your agents and capture what they actually do before proposing a single change.
Evals before opinions
I build trajectory and false-green evals so findings are numbers you can re-run, not vibes.
Evidence-binding
Each result is tied to the evidence that earned it; unsupported greens are treated as failures.
Gated remediation plan
The fix list is sequenced for a gated rollout — trust tiers and budget guards, with a fast path back.
Timeline
- 01
Phase 0 — Kickoff & access
Days 1–2Scope confirmation, read-only access, and a shared definition of “production-ready” for your system.
- 02
Phase 1 — Instrument & measure
Week 1Capture real trajectories, build the eval suite, and meter false-greens and cost.
- 03
Phase 2 — Analyze & report
Week 2Findings, severity, evidence, and the prioritized fix list — delivered as a written report and a readout call.
Who it’s for
- You run LLM agents in — or close to — production and need to trust them.
- You have a mainstream Python or TypeScript agent stack.
- You want a fixed price and a fixed scope, not an open-ended engagement.
- You’re at the idea stage with no working agent to measure yet.
- You want implementation capacity — this is an audit, not a build.
- You’re looking only for prompt tweaks rather than reliability engineering.
ScopeThe flat price covers mainstream Python/TS agent stacks (LangChain/LangGraph, custom orchestrators, OpenAI/Anthropic/Vercel AI SDKs). Custom or unusual stacks require a short scoping call first so the fixed scope stays honest.
FAQ
- What access do you need?
- Read-only by default: the repository, run logs/traces, and a way to replay representative agent runs. Write access is only requested if we later agree on a change, and it stays scoped and gated.
- What if the audit finds nothing serious?
- That’s a valid — and reassuring — outcome. You still get the eval suite and the report documenting what was tested and why it holds, which is exactly what you want to show stakeholders.
- How long does it take?
- About two weeks end to end, on the phased timeline above. Scheduling depends on access being ready at kickoff.
- What happens after the audit?
- Most teams take the fix list and run it themselves. If you’d rather I own the reliability and cost work ongoing, the audit fee is credited against a Fractional AI Architect retainer started within 60 days.
Ready to start?
Book a 30-minute systems call and we’ll confirm scope and timing.