How do we validate the correctness of responses from an AI agent? What methods or metrics do we use to test whether the agent's answers are accurate?

Validating an AI agent’s answers involves a combination of automated tests and human review. First, create a test harness that feeds a curated set of prompts to the agent and captures the responses. Use unit tests to check for syntax, expected keywords, and response length. For semantic correctness, employ automated metrics such as BLEU, ROUGE, or cosine similarity against reference answers. If the agent is a retrieval‑augmented system, verify that the retrieved documents match the query intent. For higher‑level validation, set up a human‑in‑the‑loop workflow: domain experts review a sample of responses and rate them on accuracy, relevance, and tone. Store these ratings in a database and compute an overall accuracy score. Additionally, monitor for hallucinations by checking for unsupported claims or factual errors. Finally, integrate continuous evaluation into your CI/CD pipeline so that every new model version is automatically benchmarked against the test suite before deployment.