"How do you ensure a production AI agent stays safe, accurate, and debuggable?"

This is the question that separates a playground demo from a system someone will trust with real users. Deliver it in about 90 seconds.

Beat 5 is the most commonly missed — and the most telling.

1. Guardrails — named as both input AND output, not just one side
2. Evals — golden dataset + regression testing as a deployment gate
3. Tracing — every agent step logged: inputs, outputs, tool calls
4. Metrics — cost, latency, failure rate per feature/flow
5. Human feedback loop — a detection mechanism, not just prevention
6. Regression gates — failing evals block deployment, just like failing tests

"I think about production AI safety in three layers. The first is guardrails — validation that runs before and after every LLM call, in code, not the system prompt, so it can't be overridden by a clever user. Input guardrails block injection, off-topic requests, and PII; output guardrails validate format, safety, and grounding. The second is evals — I maintain a golden dataset and run automated regression evals on every prompt change, using LLM-as-judge calibrated against human raters, and treat a failing eval as a deployment blocker, just like a failing unit test. The third is observability — tracing every agent step, logging cost and latency per feature, and maintaining a human feedback loop so gradual drift surfaces before it becomes a user-visible crisis. Guardrails prevent known failure modes. Evals catch regressions. Observability explains anything that gets through."