ai-agent-reliability

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

Required Inputs

Ask for these if not provided:

• The agent — what it does, what tools/actions it takes, what it touches
• The stakes — what a failure costs (drives how hard to test and gate)
• Where it fails now — the flakiness you've seen (points at the weak spots)
• Your setup — the framework/tools, and whether you can add evals/monitoring

Framework: Map Failures, Catch Each, Earn Trust

1. Enumerate the failure modes. Walk the agent's path — input, reasoning, tool calls, output, actions — and name where each step can break. You can't guard what you haven't named.
2. Attach a check to each. Validation for input, verification for output, schema checks for tool calls, evals for quality, gates for consequential actions — a specific catch per failure.
3. Build real evals. A set of representative and hard cases, scored — so you know it works and catch regressions before users do.
4. Gate by consequence. Irreversible or external actions get a human check; low-stakes steps run free. Match the gate to the cost.
5. Right-size it. Don't over-engineer a low-risk helper or under-test a system that moves money or data — effort follows stakes.
6. Roll out to earn trust. Shadow mode, then low-stakes live, then expand — with monitoring and alerts — so reliability is proven, not assumed.