Production Failure Triage Loop

Problem

Production failures have different causes that need different fixes, but an undifferentiated incident stream hides that. A tone complaint, a tool misconfiguration, a stale data source, and a genuine coverage gap all look like 'the agent got it wrong', so each is debugged by hand and routed ad hoc. The link between a live failure and the design change that would fix it stays broken, the monitor-to-fix loop runs slow, and a slow loop caps how fast the agent can safely take on more use cases.

Solution

Define a small, stable taxonomy of failure classes up front — for example tone and brand alignment, logic and tool errors, data quality, and coverage gaps, or a research taxonomy such as MAST (specification flaws, agent misalignment, termination gaps). Every production failure is classified into exactly one class, by an automatic classifier over traces, by human triage, or by both. Each class is wired to a fixed remediation path so the fix is dispatched mechanically rather than re-decided each time: tone goes to a system-prompt or few-shot edit, a logic error goes to a tool or config fix or to converting the step into deterministic code, a data-quality failure routes back to the data owner, and a coverage gap opens scope work or an escalation hand-off. The latency of the loop — failure observed to fix shipped — is tracked as a first-class metric, because that speed, not any individual fix, is what gates how fast the agent can take on more use cases.

When to use

• The agent is live and producing a steady stream of real-world failures that need continual fixing.
• Failures have distinct causes that map to distinct fix surfaces — prompt, code, data, or scope.
• The team needs to know whether its fix loop is fast enough to justify scaling to more use cases.