Stress-Testing AI Agents With Adversarial Golden Trajectories Before Deployment

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A DEV Community article, the fourth in a five-part series on evaluating production AI agents, argues that test suites built from happy-path scenarios fail to expose real-world weaknesses. It proposes curating adversarial 'golden trajectory' datasets across four categories: infrastructure failures, ambiguous instructions, indirect prompt injection, and state conflicts. Examples include how an agent should respond to 503 errors or 429 rate limits, and whether it asks for clarification when two contacts share the same name. The author also recommends testing whether agents execute malicious instructions hidden in untrusted tool output. The piece frames these checks as essential to catching structural vulnerabilities before real users encounter them.

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