Building Reliable AI Agents: Lessons from Production

1-minute read
ai-agents • reliability • llm • production • full-stack

Production agents fail in boring ways: timeouts, tool errors, runaway loops, and silent wrong answers. Reliability engineering applies to agents too.

Hardening Checklist

  • Max steps and token budgets per session
  • Idempotent tools with clear error messages
  • Checkpoint state for long workflows
  • Circuit breakers when external APIs fail
  • Structured logging of every tool call

Graceful Degradation

When the agent fails, fall back to search-only RAG or human handoff-never an empty error.

Testing

Record replay fixtures of tool responses. Property-test parsers. Red-team prompt injection on tool descriptions.

Conclusion

Reliable agents are mostly reliable orchestration-constraints, observability, and fallbacks-not smarter prompts alone.