Demystifying evals for AI agents
01:00 · January 9, 2026 · Antropic Engineering Blog
The capabilities that make agents useful also make them difficult to evaluate. The strategies that work across deployments combine techniques to match the complexity of the systems they measure. \n
Summary
Good evaluations allow teams developing AI agents to detect behavioral changes and failures during development rather than after deployment. Agents differ from single-turn language models because they execute sequences of tool calls, update external state, and adapt to intermediate results; these same traits make errors compound and allow models to discover solutions outside the bounds of a static test, such as finding an unstated policy loophole while attempting a flight-booking task.
Evaluations rely on three grader families—code-based, model-based, and human—applied to either the final outcome or the full transcript. Coding agents are typically scored with deterministic unit tests against benchmarks such as SWE-bench Verified, which supplies real GitHub issues and accepts a patch only when the repository test suite passes. Conversational agents add a second model to simulate the user and combine state checks, turn-count constraints, and rubric-based judgments of tone or instruction following, as implemented in τ2-Bench. Research agents require groundedness, coverage, and source-quality checks because correctness is context-dependent and reference material changes over time.
Capability suites deliberately start with low pass rates on hard tasks to guide improvement, while regression suites target near-100 % reliability to guard against regressions. Once capability tasks reach high reliability they can migrate into the regression set. The same infrastructure also supplies baselines for latency, cost, and token usage, and supplies a shared quantitative language between product and research teams. Teams adopt this structure at different points—some encode expected behavior before any code is written, others introduce it once production scale makes manual review unsustainable—but the resulting signal compounds across the agent’s lifetime.
Why it matters
Directly actionable for Product Teams and Builders developing AI agents, with concrete techniques, code examples, and lifecycle considerations that align with ethical and reliable AI deployment priorities in the Dutch market.
