How to Evaluate AI Agents Without Breaking Them

Evaluating AI agents presents a fundamental puzzle: traditional software testing s often fail because agents exhibit non-deterministic behavior, creative problem-solving, and complex multi-step reasoning that breaks conventional pass/fail frameworks. The checklist reveals that teams frequently misdiagnose infrastructure issues as reasoning failures, with timeouts, malformed API responses, and stale caches masquerading as agent errors. This confusion creates evaluation systems that either stop progress by only guarding existing behavior or ship regressions by chasing new capabilities without proper safeguards.

The solution centers on separating capability evaluations from regression evaluations, with each serving distinct purposes in the development lifecycle. Capability evals push agents forward by measuring progress on difficult tasks, while regression evals protect what already works. Without this separation, teams either stagnate by only testing existing behavior or introduce regressions by focusing solely on new capabilities. The checklist emphasizes that evaluation must begin with manual inspection of 20-50 real agent traces before building any automated infrastructure, as this reveals failure patterns no automated system can detect initially.

Ology starts with three evaluation levels mapped to observability primitives: single-step (run), full-turn (trace), and multi-turn (thread). Teams should begin with trace-level evaluations that grade complete agent interactions across correctness, helpfulness, and safety dimensions. For agents that perform actions rather than just generate text, state change evaluation becomes critical—verifying that scheduled meetings actually appear in calendars or that written code executes correctly. The checklist recommends starting simple with end-to-end evals that test core task completion, then adding complexity only when simpler approaches miss real failures.

Dataset construction requires unambiguous tasks with reference solutions that prove solvability, testing both positive cases (behavior that should occur) and negative cases (behavior that should not occur). Teams should manually create 20 example inputs covering key dimensions of variation, then establish ongoing pipelines using dogfooding errors, adapted external benchmarks, and hand-written behavior tests. The checklist warns against tasks where agents cannot possibly succeed due to missing information or impossible constraints, noting that such tasks are broken rather than the agent being evaluated.

Grading systems should default to code-based evaluators for objective checks, reserving LLM-as-judge for genuinely subjective assessments and human review for ambiguous cases. Binary pass/fail grading forces clearer thinking than 1-5 scales, though recent research suggests short scales may improve human-LLM alignment specifically. Crucially, graders should evaluate outcomes rather than exact paths, building in partial credit for incremental progress and avoiding requirements that fail agents finding creative solutions. Custom evaluators derived from error analysis catch specific failure modes better than generic off-the-shelf metrics.

Operational implementation requires distinguishing between offline, online, and ad-hoc evaluation, using all three throughout development and production. Multiple trials per task account for non-determinism, with clean isolated environments ensuring independent . Teams should tag evals by capability category, document what each measures, and track efficiency metrics like step count, tool calls, and latency alongside quality metrics. When pass rates plateau, test suites must evolve with harder tasks or new capabilities rather than grinding on saturated evaluations.

Production readiness involves promoting capability evals with consistently high pass rates into regression suites integrated into CI/CD pipelines. Versioning prompts and tool definitions alongside code enables correlation between changes and performance impacts. Production failures must feed back into datasets and error analysis, creating a continuous improvement flywheel. The checklist emphasizes that tool interface design often eliminates entire classes of errors more effectively than prompt optimization, with examples like requiring absolute file paths to prevent navigation errors.

Limitations include of evaluating multi-turn conversations, which the checklist identifies as the hardest level to implement effectively. LLM-as-judge grading for objective tasks can be unreliable, with inconsistent judgments masking real regressions. The approach requires significant manual effort initially, particularly in error analysis where teams should spend 60-80% of evaluation resources. Success depends on having a single domain expert own the evaluation process rather than designing by committee, maintaining datasets, recalibrating judges, and deciding what constitutes "good enough" performance.