Anthropic’s own guidance reflects all of this. Agents are “fundamentally harder to evaluate” than single-turn chatbots because they operate over many turns, call tools, modify external state, and adapt based on intermediate results. And so the guidance is to grade outcomes, transcripts, tool calls, cost, and latency as separate dimensions, while running multiple trials and keeping capability evals cleanly separated from regression evals (which should hold near 100% and exist to prevent backsliding).
The improvement loop
The shape of a working improvement loop is starting to converge across vendors. LangChain’s April update shipped more than 30 evaluator templates covering safety, response quality, trajectory, and multimodal outputs, plus cost alerting and a serious push toward human judgment in the agent improvement loop. Karpathy’s autoresearch experiment, in which an agent ran 700 experiments over two days against its own training code with binary keep-or-revert decisions, makes the same point in a different way. Most AI developers underinvest in measurement, and the eval is the product.
Strip away the tools and the loop is simple: Production complaint becomes trace, trace becomes failure mode, failure mode becomes eval, eval becomes regression test, and regression test becomes release gate. Then, and only then, do you change the prompt, swap the model, adjust the retrieval strategy, or tune the cost/latency trade-off.
