How to Build an AI Agent Testing Automation Workflow Define your agent output schema using Zod for runtime validation of tool name, parameters, and confidence score. Mock the LLM layer via dependency injection so unit tests run deterministically without API calls. Write unit tests covering every tool-routing path, including edge cases like malformed output and LLM failures. Create an evaluation fixture set with input/expected-output pairs spanning each tool, ambiguous queries, and adversarial inputs. Build an eval runner that scores agent responses against fixtures using exact-match and schema validation. Configure CI via GitHub Actions with unit tests gating eval runs and a 90% pass-rate threshold blocking merges. Deploy a React dashboard to visualize eval results, pass rates, and regressions over time. AI agent testing automation is becoming an unsolved problem in modern development workflows.…