Enables any AI agent framework to plug into a self-improvement loop where recall and record calls turn existing feedback like tests, CI, review bots and user corrections into learned skills and persistent memory. Changes are governed end-to-end with changesets, canary rollout on live traffic, holdout lift measurement, versioning and rollback.
Bringing the debugging we know and love as human programmers to our AI agents – debug any supported language with breakpoints, variable/state inspection, and stepping, to supercharge agents' capabilities to reason about runtime code.
Provides coding agents with visibility into test health through tools for flaky test detection, test quality linting, and LLM evaluation harness, enabling them to triage failures, review test quality, and check prompt changes for regressions.
Run dynamic, multi-agent workflow scripts — agent(), parallel(), pipeline() — over real coding agents (Claude Code and OpenAI Codex), with deterministic journaling, resume, token budgets, and git-worktree isolation.