What it looks like in your seat.
Same platform, four very different jobs. Here's exactly what you do with Runback, what you get, and where to start.
You run the platform agents are deployed on. When one misbehaves in production you get paged — and the logs can't tell you which decision went wrong, or why.
- Connect agents to a Runback you run inside your own VPC — the SDK (≈3 lines) or your existing OpenTelemetry exporter.
- When an incident hits, open the run, jump straight to the failed step, and replay it to reproduce the exact behavior.
- Export a signed audit record for the post-incident review.
- Incident root-cause in minutes, not an afternoon in the logs.
- Nothing leaves your perimeter — so it clears the bank's security review.
- A tamper-evident record for the incident write-up.
You want to move to a cheaper or newer model — but you can't tell if it'll quietly degrade your agent's answers until it's already in production.
- Capture your real production runs as they happen.
- Replay a run — or a whole dataset of them — against the new model, and compare answers side by side on your actual cases.
- Promote the change only when the evals still pass.
- Model swaps backed by evidence on real traffic, not a vibes check on a toy prompt.
- Catch cost-cutting that breaks behavior before your customers do.
- A repeatable way to vet every new model release.
Your teams ship agent features fast. You need confidence that a prompt or model change didn't quietly break customer-facing behavior between releases.
- Capture every team's agent runs as they go out.
- Turn known-good behavior — and any past failure — into evals with one click from a real run.
- Run the eval suite as a release gate; a regression fails the build before it reaches a customer.
- Fewer production incidents, and faster root-cause when one slips through.
- A reliability signal you can actually report upward — pass rate per release.
- Teams ship agent changes with confidence, not crossed fingers.
You have to sign off on AI agents going to production — and prove to auditors and regulators that a control was in place when one made a decision.
- Every agent decision is captured with the exact context the model saw, PII redacted in-process.
- Reproduce any decision on demand, and export a signed, tamper-evident audit record.
- Map the evidence to the controls you already answer to — CPS 230, EU AI Act, NIST AI RMF.
- Audit-ready evidence for any agent decision, on request.
- Data residency — it all runs in your environment.
- A control you can point to, not a hand-wave.
Not sure which is you?
Open a real run and walk it yourself — then tell us what you're trying to ship.