@Vtrivedy10: my fave point from here: the earlier you think about your agent as a system that can be measured & improved, the faster…

my fave point from here: the earlier you think about your agent as a system that can be measured & improved, the faster you can get a robust agent into production This isn’t just a technical thing, it’s a human & team thing. Teams that succeed here ask questions like: - “what do I need my agent to do to make our customers happy?” - “What scenarios will my agent encounter in the wild and how can we recreate that in our testing” Evals are the substrate that determines what your agent does in production. They’re training data for agents because we literally fit our agent to pass Evals via hill-climbing algorithms and human edits to pass failure modes Once you get your agent into users hands, the eval generation loop compounds. Production data uncovers more issues, these issues turn into Evals, and the agent fits to improve over more cases that could not be captured without real user data. In the early stages, teams dogfooding their product becomes the feedback signal Curating evals and running experiments on different agent variants is a muscle every team develops, our goal is to create tooling so every team can create the best agents for their tasks using this foundation