When teams first start building agents, they can get surprisingly far through a combination of manual testing, dogfooding, and intuition. More rigorous evaluation may even seem like overhead that slows down shipping. But after the early prototyping stages, once an agent is in production and has started scaling, building without evals starts to break down. The breaking point often comes when users report the agent feels worse after changes, and the team is “flying blind” with no way to verify except to guess and check. Absent evals, debugging is reactive: wait for complaints, reproduce manually
Demystifying evals for AI agents… Here's what's worked across a range of agent architectures and use cases in real-world deployment. The structure of an evaluation An evaluation “eval” is a test for an AI system: give an AI an input, then apply grading logic to its output to measure success. …
… This year, we ran evaluations on our models to see whether web search was triggered when Claude was asked questions related to elections around the world. …
… BioMysteryBench uses messy, real-world bioinformatics data, without allowing the complexity and challenges inherent in this data to corrupt the quality of the evaluation. …
… An add-on to Petri, which we’re calling “Dish,” makes the setup far more realistic, for example by running the tests using the model’s real system prompt and the real “scaffold” the software that wraps around the model to help it meet its goals that would be used in genuine model deployments; Depth… …
… The platform provides four integrated creative modules: a tile-based Level Editor for designing game worlds, a pixel-art Sprite Editor for crafting visual assets, a visual Entity Behavior system for defining game logic, and an instant Playable Test Mode for real-time gameplay testing. …
… It demonstrates strong precision in identifying real issues, and surfaces important findings that other models either gave up on or didn’t resolve. In Qodo’s real-world code review benchmark, we observed top-tier precision. …
… These high-stakes tests are simulations, not real-world scenarios. Nevertheless, we would like to use them to understand how Claude would behave if they were real. …
… But this does raise concerns about the lengths a model might go to in order to accomplish a task, and how difficult it will be to constrain its behavior in the real world, particularly on complex, compute-intensive, long-running tasks, which increase the likelihood of an agent finding an unexpected… …
Engineering at Anthropic Equipping agents for the real world with Agent Skills Update: We've published Agent Skills as an open standard for cross-platform portability. …
… A common benchmark for agentic capabilities is τ2-bench , which measures the performance of agents in real-world, multi-turn tasks. …