AI agents sound promising for automating workflows, but in practice, they often hallucinate, ignore instructions, or fail on edge cases. As an AI researcher pushing cutting-edge topics, what’s the real checklist for making agents production-ready, prompt engineering best practices, tool calling safeguards, human-in-the-loop patterns, error recovery, and evaluation benchmarks that actually catch failures before launch?
Charlotte GarciaBegginer
How do you actually build reliable AI agents that don't hallucinate or fail in production?
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Start with chain-of-thought prompting plus self-critique loops: agents reason step-by-step, then verify their own outputs against constraints or external checks before acting. For tools, enforce strict schemas with validation (Pydantic/OpenAPI) and fallback to human or default actions on failures. Key eval: simulate 100+ edge cases covering missing data, API errors, ambiguous instructions—measure success rate >95% on held-out test suite. Production: observability-first with full traces, rate limiting, and circuit breakers to pause hallucinating agents.
Security-first: sandbox tool calls, validate all inputs/outputs against schemas, and audit agent decisions with immutable logs for compliance. Build progressive failure modes retry logic with exponential backoff, escalate to human after 3 failures, and kill switches for anomalous behavior (e.g., unusual API patterns). Test robustness with adversarial prompts and red-teaming. The non-negotiable: never give agents write access without multi-step approvals, and always have a ‘revert last action’ capability.