AI & Technical question

How would you measure the reliability of agents built on LangGraph in production?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Whether the candidate can define reliability for a non-deterministic system, not just borrow uptime metrics from traditional software.

Clarify with the interviewer whether reliability means task success, infrastructure uptime, or both, since LangGraph spans both concerns.
Separate infrastructure reliability, like node execution failures and retries, from task-level reliability, like the agent reaching the correct final state.
Propose a metric tree: task completion rate, step-level error rate, human-intervention rate, and time-to-recovery after a failed step.
Explain how you would instrument this with LangSmith traces to attribute failures to a specific node, tool call or model response.
Address guardrails: cost per successful run and latency, since a system can be reliable but too slow or expensive to use.

Distinguishes silent failures, where the agent completes but gives a wrong answer, from loud failures like exceptions, and proposes a way to catch the former, such as LLM-graded evals on a sample.
Suggests segmenting reliability by agent complexity, for example single-tool versus multi-step graphs, since a single blended number hides where it breaks.
Proposes a concrete guardrail, like human-intervention rate staying under an assumed 5 percent for the workflow to be considered production-ready.
Ties the metric back to a business consequence, such as support ticket volume caused by agent errors.

Reusing generic uptime or latency metrics without addressing task correctness.
Ignoring that many agent failures are silent and require sampled human or LLM review to detect.

How would you catch a silent failure where the agent completes but gives a wrong answer?
How would this metric tree change for a multi-agent system versus a single agent?