Agent Evaluation Readiness Checklist

While everyone is talking about using AI to automatically evaluate AI, LangChain's guide brings us back to earth: before building any automated evaluation system, you must first learn to manually understand failures. Why does this matter? Because there's a common misconception in agent development today—teams rush to build complex automated testing pipelines without a basic understanding of why their agents fail. It's like a doctor ordering a full battery of tests without first taking a patient history—wasting resources while missing the core issues. At its heart, this checklist follows a 'diagnose first, prescribe later' approach. The first step is manually reviewing 20-50 real agent traces. Why this number? Experience shows it's enough to cover most common failure patterns without overwhelming you with data. LangChain recommends their LangSmith tool for this process, but the core idea is universal: before you understand failure patterns, automation will only help you make the same mistakes faster. The second step is defining clear success criteria. Here's a practical test: if two experts disagree on evaluating the same task, the task description itself is problematic. For instance, 'summarize this document well' is vague, while 'extract the 3 main action items from this meeting transcript, each under 20 words and including the owner if mentioned' is specific. This precision isn't nitpicking—it's the foundation for any evaluation system that actually works. The third insight is particularly counterintuitive: you must separate capability evaluations from regression evaluations. Capability evals measure what your agent can do, typically starting with low pass rates and giving you room to improve. Regression evals ensure existing functionality doesn't break, maintaining near-100% pass rates. Many teams conflate these, resulting in either stagnation from fear of breaking things, or shipping regressions while chasing new capabilities. This checklist reveals a deeper trend: agent evaluation is shifting from 'testing code' to 'understanding behavior.' In traditional software testing, inputs and outputs are relatively deterministic—you mainly verify logical correctness. But agent behavior is emergent and uncertain, making evaluation fundamentally about understanding 'why failures occur' rather than just 'whether they occur.' LangChain suggests spending 60-80% of evaluation effort on error analysis—a proportion that might surprise many, but reflects the fundamental difference between AI systems and traditional software. Practically speaking, readers can immediately apply this self-check process. Before launching any evaluation project, ask yourself six questions: Have I manually analyzed enough failure cases? Are my success criteria clear? Have I separated capability and regression tests? Can I clearly explain each failure's cause? Have I ruled out infrastructure issues? Do I have a clear evaluation owner? If you can answer yes to all these, your evaluation system has a solid foundation. There's another easily overlooked angle: infrastructure issues often masquerade as reasoning failures. LangChain cites a case where a single extraction bug moved benchmark results from 50% to 73%. Timeouts, malformed API responses, stale caches—these engineering problems frequently get mistaken for the agent's 'thinking ability' being insufficient. Before blaming the model, check your data pipeline—a step many teams overlook. Ultimately, the value of this checklist isn't in providing cutting-edge technical solutions, but in emphasizing evaluation fundamentals. Like any professional field, master the basics before pursuing complex solutions. For teams building agent systems, this is a practical guide worth printing and pinning to the wall.