Trace. Experiment. Test. Evaluate. Improve. Monitor. Run again.
Model updates, prompt edits, tool changes: agent behavior shifts under your feet, and it shifts in production first.
The evidence of every shift lands in your traces, at a volume no team can inspect by hand. What broke, who it broke for, and which cases to pin down are all recorded and unread.
RunAgain closes that gap. Trace what happened, evaluate what changed, debug and replay it, and turn every issue into a test that outlives the next change.
Every agent run, production or local, streams into RunAgain as a structured trace: every step, prompt, tool call, token and millisecond. Search across runs, diff any two, and replay the weird ones.
RunAgain watches your traces and drafts the boring parts automatically: tests from real runs, mocks from recorded tool calls, eval suites from failure patterns. You review each suggestion and approve.
12 production runs looped on duplicate refund requests. Drafted a regression test from run c9e0 with mocked stripe.refund.
Answers started citing docs that weren't retrieved. Drafted a judge prompt scoring answer-to-context faithfulness on every run.
search API is flaky (7% timeouts) and paid. Recorded 214 real responses so tests and experiments replay them for free.
Failed evals, tool errors and drift post to your channel as digests, with drafted tests and fixes ready to review.
Run agents against tool responses simulated from previous runs: deterministic, fast, and no burned API credits.
LLM-as-judge, rubrics, assertions, trajectory scoring, pairwise diffs, dataset regression, human review, or bring your own.
Tests, mocks and eval suites drafted from your real traces. You review and approve.
Get paged when quality, cost or latency drifts from baseline, before customers churn over bad behaviour.
RunAgain ships as an MCP server: run experiments, tune evals and approve suggestions straight from Claude, and export your best runs as fine-tuning datasets.