Production Failure Triage Loop

also known as Failure-Class Routed Remediation, Post-Launch Triage Loop

Sort every production agent failure into a small fixed taxonomy and bind each class to a set remediation path, so fixes are dispatched mechanically and the monitor-to-fix loop stays fast enough to gate scaling.

Context

An agent runs in production and failures arrive continuously from real traffic — wrong answers, broken tool calls, off-brand tone, and requests it was never scoped to handle. Most of the work now sits after go-live, not before it. When each failure is investigated from scratch, remediation is slow and the team cannot tell whether the agent is improving or drifting.

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.

Forces

A small fixed taxonomy makes failures comparable and routable, but too coarse a scheme lumps unlike causes together while too fine a scheme is unstable and hard to classify.
Automatic classification scales with volume, but a misclassified failure is routed to the wrong fix-path; human triage is accurate but does not keep up with traffic.
Each failure class wants a different remediation surface — prompt, code, data, or scope — and routing to the wrong surface produces a fix that masks the cause instead of removing it.
The speed of the loop, not the cleverness of any single fix, is what gates scaling, so triage overhead competes directly with throughput.

Example

A support agent handling thousands of cases a day starts producing more escalations. Instead of debugging each ticket on its own, the team tags every failed case with one label — tone, logic, data, or coverage. Tone cases go to a prompt tweak, logic cases to a tool fix, data cases back to the data team, and coverage cases to a new sub-flow. Within a week they can see which bucket is growing and how long each fix takes to ship.

Diagram

flowchart TD T[Production traces] --> D{Failure detector} D -->|failure| C[Classify into fixed taxonomy] C -->|tone/brand| P[Prompt or few-shot edit] C -->|logic/tool| K[Tool/config fix or deterministic code] C -->|data quality| O[Route to data owner] C -->|coverage gap| S[Scope work or escalation] P --> R[Redeploy] K --> R O --> R S --> R R --> M[Track loop latency] M --> T

Solution

Therefore:

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.

What this pattern forbids. Every production failure must be assigned exactly one taxonomy class before remediation; a failure that cannot be classified must not be silently dropped, and no fix may be dispatched outside its class's bound remediation path.

The smaller patterns that complete this one —

usesRouting★★— Classify an incoming request and dispatch it to the specialist (lane / agent / model) best suited to handle it.

And the patterns that stand alongside it, or against it —

complementsPostmortem Pattern Mining★— Mine a corpus of thousands of written postmortems through a staged model pipeline that summarises, classifies, analyses, and aggregates so that recurring incident causes surface as one short report.
complementsRe-Contact-Subtracted Resolution Gate★— Gate a support agent on a re-contact-subtracted resolution rate so an interaction that merely ends the session is never reported as a resolved one.
complementsTyped Refusal Codes★— Define a single source of truth for machine-readable refusal codes across all guard surfaces, so refusals can be triaged mechanically rather than by string-grepping ad-hoc human-readable messages.
complementsDeterministic Control Flow, Not Prompt★— Branching decisions live in deterministic application code while the LLM is invoked at strategic points to produce structured signals that the code branches on.
complementsPolicy-as-Code Gate★— Evaluate every proposed agent action against externally-managed machine-readable policies before dispatch, so compliance authorship lives outside the prompt and outside the agent code.
complementsSymptom-Remediation Thrashing✕— Anti-pattern: a stateless auto-remediation agent repeatedly applies symptom-level fixes that hit the target metric while masking the root cause and suppressing the page, so the underlying fault compounds across incidents into a larger outage.