Your agent didn’t crash.
It just started being wrong.
Every request returned 200.
A user found out before you did. Zespan traces every span, catches the regression, and hands you a fix tested against the failure that caused it.
Zespan detects01 what your agents got wrong, prevents02 it happening twice, and owns the fix03 from first signal to production.
Most AI observability tools were built to monitor LLM calls. Zespan was built for the world where agents plan, delegate, use tools, and make autonomous decisions.
01
Know the moment quality slips.
Verdict clustering groups failures by what actually went wrong, and anomaly detection catches cost and latency drift as it starts.
12,480 traces · 1.8% error rate
02
Stop it happening twice.
The failing trace becomes an enforced policy in one action. Near-misses are captured and every handoff is scored on a trust ledger.
9 protections · 82 checks
03
Ship the fix, gated on approval.
A candidate replayed against the failures the incident captured, scored on cost and quality. Nothing ships automatically.
39/41 now pass · 2 regressions flagged
1.0 Detect
Know the moment quality slips,
not when a user complains.
Full-stack understanding
Every agent run is captured the moment it happens, with no sampling and no manual instrumentation. Verdict-based clustering groups failures by what actually went wrong, and AI correlation ties the alert to the trace with a root cause already attached.
2.0 Prevent
Turn the trace that broke something
into a rule that stops it twice.
Gated by default
Flag a trace and it becomes an enforced guardrail policy in one action, no separate config step. Twelve built-in LLM-as-judge templates score every trace, and near-misses are queued as suggested rules for a human to approve.
3.0 Fix
Ask it anything, then let it
ship the fix you approve.
Human in the loop
ZespanPilot answers in plain English and takes action across ~40 registered operations, every mutation role-gated and audit-logged. When an incident closes, Zespan proposes the fix and replays it against the failures that incident captured.
The closed loop
When an incident closes, Zespan generates a fix candidate and replays it against every failure that incident actually captured, so you see the pass rate before you decide anything.
Every approved or rejected candidate becomes a permanent regression test, pulled from a real production failure, never a synthetic one. Nothing ships automatically.