Find Your AI's Failure Points Before Your Users Do
A 50-point self-assessment checklist for teams shipping agents, RAG, and AI copilots. Built to surface the exact reliability gaps that traditional testing and APM can't see, before they become a production incident.
Built for the team that already shipped, not the one still planning.
You've shipped an LLM feature and have a nagging feeling you don't actually know where it's fragile. The demo worked. Production is a different question, and nobody's answered it yet.
You found out about an AI failure from a customer, a Slack screenshot, or a support ticket — not from your own monitoring. If that's happened once, it will happen again in the same blind spot.
You're about to ship an agent, RAG pipeline, or AI copilot and want a real gap-check before launch — not a checklist that stops at "did we write a good system prompt."
Fifty checks. Eight categories. Zero fluff.
Whether your eval set actually catches adversarial input, or only ever sees the happy path your demo used.
The fail-open-vs-fail-closed decision most teams never make on purpose, and what happens the first time it matters.
The exact citation-checking bug that makes a system confidently cite a fact a document explicitly says doesn't exist.
Why "the request succeeded" and "the answer was right" are different metrics, and why most dashboards only track the first one.
Plus four more categories covering prompt/model management, human-in-the-loop escalation, deployment, and incident response.
Michael Legemah is a Principal AI Engineer who has spent over a decade building production systems for AWS, the U.S. Army, and U.S. Space Force. The last several years focused specifically on agentic AI, RAG pipelines, and the evaluation infrastructure that keeps them honest. This checklist comes directly from failures he's diagnosed and fixed inside real production systems, not from a summary of someone else's blog posts.
Fifty checks. One afternoon. A concrete list of what to fix first.
Get the Checklist — $49