Explain an AI Agent You Built: Design, Evaluation, and Governance

# Explain an AI Agent You Built: Design, Evaluation, and Governance Describe an AI-agent project you actually worked on. Explain the agent's purpose, the design decisions you owned, how you evaluated it, which evaluation metrics you used, and how you handled data governance and hallucinations. If your experience is a prototype, say so and distinguish demonstrated behavior from proposed production safeguards. No particular model, agent framework, dataset or deployment scale is assumed. Your explanation should connect the architecture to the task the agent performs. Discuss where generated text or proposed actions must be checked before they can affect a user or an external system. Explain how evaluation would reveal failures that a plausible-sounding response can conceal. ### What a Strong Answer Covers - Your specific contribution and the boundary between the model, tools, retrieved data and decision logic. - A task-level evaluation set, measurable success criteria and failure categories that include unsupported claims or actions. - Governance across input data, access controls, logs, retention and evaluation examples. - How hallucination mitigations are tested and when the agent should abstain or seek human help. ```hint Evaluate the whole task A fluent final response is not evidence that tool use was authorized or that the underlying task succeeded. ``` ### Follow-up Questions - How would you distinguish a retrieval failure from an unsupported claim generated despite correct evidence? - What evidence would justify moving a prototype to broader use?