// UNDERSTAND · DEBUG AI AGENTS
From “it did something weird” to the exact span that did it.
Search the run, open the trace, and read the failure like a stack trace. RunAgain names the likely cause, diffs the run against the last good one, and turns the bad case into a test so it never comes back.
THE PROBLEM
Reproducing agent bugs is the whole bug
The user says the agent "went off the rails." Which run? Which of the twenty steps? Was it a bad tool result, a truncated response, or a prompt change last Tuesday? Without the trace, debugging an agent is archaeology.
// HOW RUNAGAIN SOLVES IT
Failed runs are classified as an errored span, a failed tool call, a truncation, a dangling tool call, or silent no-output, so the trace detail tells you where to look before you start reading.
Changed runs are matched to the previous run of the same agent: system prompt and final output diffs show exactly what shifted, separating a code regression from model weather.
Filter to Errors or Slow with one click, search by user, agent or trace id, follow the Slack alert's deep link: every path lands on the offending span with its full input and output payloads.
Capture the failing trace into a versioned dataset and it becomes a permanent experiment case, and the fix is verified against the actual production failure, forever.
A flagged span, pinpointed in the run that produced it.
COMMON USE CASES
RELATED SOLUTIONS
Be first to run again.
Book 30 minutes and see the loop on your own agents, or write to tamas@runagain.ai.