Foundry

Teams use Foundry to turn everyday web-app work into repeatable agent runs. A typical flow starts by capturing a real process—like refund handling in a helpdesk, screening candidates in an ATS, or updating records in a CRM—then expressing it as a task the agent must complete inside a controlled browser environment. Because the simulator stays consistent, you can rerun the same scenario after every prompt change, tool update, or model swap and see whether the agent improved or regressed.

During development, engineers run batches of tasks to surface where the agent stalls, clicks the wrong element, or misreads page state. Traces and outcomes make it easy to pinpoint the step that caused failure, adjust the policy or instructions, and immediately validate the fix on the same set of cases. When edge conditions matter—different user roles, missing fields, slow-loading pages, or alternate UI paths—you can set up variations and verify the agent handles them without relying on the live web.

Foundry is also used as a feedback pipeline. Reviewers label key moments in an agent run, mark errors, and attach guidance so the next training or tuning cycle targets the right behaviors. Over time, the collected examples become a dataset for reinforcement learning or other improvement loops, and the evaluation results provide a running scorecard that supports release decisions and ongoing monitoring before automation is rolled out more broadly.