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
- 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.
- 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.
- Build real evals. A set of representative and hard cases, scored — so you know it works and catch regressions before users do.
- Gate by consequence. Irreversible or external actions get a human check; low-stakes steps run free. Match the gate to the cost.
- Right-size it. Don't over-engineer a low-risk helper or under-test a system that moves money or data — effort follows stakes.
- Roll out to earn trust. Shadow mode, then low-stakes live, then expand — with monitoring and alerts — so reliability is proven, not assumed.
Output Format
Agent reliability: [what it does] · stakes [level]
Failure map: [bad input · hallucination · wrong tool call · edge cases · silent errors · runaway loops]. Catch each: [failure → the check: validation / verification / schema / eval / human gate / monitor]. Evals: [the real + hard cases to test on, scored]. Human gates: [the consequential actions that need approval]. Right-sized: [effort matched to stakes — where to invest, where not]. Rollout: [shadow → low-stakes → expand, with monitoring].
Quality Checks
- Enumerates failure modes across the agent's whole path
- Attaches a specific check to each failure
- Includes evals on real and hard cases, scored
- Gates consequential actions with a human; automates low-stakes
- Scales effort to stakes; rolls out to build trust incrementally
Anti-Patterns
- Shipping a demo as if it's production-ready.
- No evals — quality assumed, regressions invisible.
- The same trust level for a summary and a money transfer.
- Gold-plating a toy or under-testing a high-stakes system.
- Big-bang launch with no shadow mode or monitoring.
Example Trigger Phrases
- "How do I test my AI agent so I can actually trust it?"
- "My automation works sometimes — how do I make it reliable?"
- "How do I put an AI workflow into production safely?"
- "What checks does my agent need before I let it run on real data?"
- "How do I know my agent won't do something dumb and irreversible?"
