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ai-agent-reliability

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Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.

skills/ai-agent-reliability/SKILL.md

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# AI-Agent Reliability

An AI agent that works in a demo and one you can trust in production are different things — the gap is everything that happens when input is messy, the model hallucinates, a tool call goes wrong, or an error fails silently. This maps where your agent can fail and the specific checks that catch each, scaled to the stakes, plus a rollout that earns trust incrementally — so "works sometimes" becomes "works reliably."

## What This Skill Produces

- **A failure map** — where this agent can go wrong: bad/unexpected input, hallucinated output, wrong or malformed tool calls, unhandled edge cases, silent failures, and runaway loops
- **The catching checks per failure** — input validation, output verification, evals on real cases, schema/format checks on tool calls, human-in-the-loop gates, and monitoring/alerts
- **An eval approach** — testing on a real set of cases (including the hard ones) so quality is measured, not assumed, and regressions are caught
- **Human-in-the-loop placement** — where a human must approve, scaled to consequence (irreversible/external actions gated, low-stakes automated)
- **A right-sized plan** — reliability effort matched to the stakes, not gold-plating a low-risk toy or under-testing a high-risk system
- **A trust-building rollout** — shadow mode → low-stakes → expand, with monitoring, rather than shipping it everywhere and hoping

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