Four phases we run on every engagement: Zero In, Evaluate, Amend, Lock. A machine surfaces failures at scale; we supply the customer-support ground truth and independent judgment it can't. Every phase leaves a permanent artifact your team can operate after we leave.
Book a 30-min audit →Trace-mining agents (LangSmith Engine, Braintrust, or our own harness) now cluster thousands of production conversations into named issues automatically. That's a tool, and tools commoditize. What doesn't: deciding which cluster actually breaks your refund policy, calibrating the judges against what 'correct' means in your domain, and signing the score as an independent party. The ZEAL loop pairs both — and runs continuously in production after handoff, not as a one-time audit event.
Surface at scale. Name what matters.
A trace-mining agent clusters your production conversations into named failure issues — no hand-reading hundreds of transcripts. Our job starts where the machine's ends: turning a generic cluster into a named, severity-rated failure mode that matters to your business — policy violation, brand breach, missed escalation, hallucinated commitment. The taxonomy is built against your actual policies, not a generic rubric.
Every AI system has a different failure signature. A retrieval-augmented support agent fails differently than a code-generation copilot. The machine can cluster failures faster than any human — but it can't tell you which cluster breaks your refund policy or whether an escalation should have fired. That judgment is the taxonomy every later measurement is built on.
Define 'correct.' Then measure it.
We convert each failure mode into a binary evaluator — pass/fail per mode, per conversation. Where judgment is deterministic (did the agent cite a refund window that doesn't match policy?), we write code. Where judgment is required (is this tone right for the persona?), we write a validated LLM-as-judge, calibrated against human labels on a held-out split. The machine proposes evaluators; we supply the ground truth that defines 'correct' in your domain and publish the validation scores — so you see exactly how much to trust each one.
Fix what the data ranks — not what feels important.
Measurement ranks the fixes. We hand you reproducible findings and the specific change each calls for — prompt edits, tool-description fixes, retrieval tuning, escalation-threshold changes, human-review insertion points. For in-house agents we can draft these as PRs against your repo; for vendor platforms (Decagon, Sierra, Ada, Intercom Fin) they're prioritized recommendations you take to your vendor — because no tool can patch a black box. Each fix is proven with a before/after experiment.
Make every failure unrepeatable.
Every confirmed failure becomes a permanent online evaluator (catches recurrence live) and an offline regression case (catches it before you ship). This is where the loop becomes self-reinforcing — each resolved issue makes the eval suite more complete, which makes the next cycle more robust. Drift monitoring keeps watching after handoff — independently, with the score reported to your leadership.
The first three phases are increasingly automatable by anyone with a LangSmith seat. Lock — operated independently, on a compounding vertical asset, with the score signed by a third party — is what a tool can't sell you. You're not hiring a consultant once; you're building a compounding reliability asset.
It's a continuously-running eval workspace — your failure taxonomy, your evaluators, your regression bench, your dashboard — branded for your system and documented so your team can operate it after we leave. Built on best-in-class infrastructure (LangSmith, Braintrust, or your existing stack), not homegrown tooling. The methodology travels. The IP is yours.
Derived from your real production traces. Not a template.
Validated against your human labels. Validation scores published.
Runs on every release. Compounds with each engagement.
Agent Reliability Score. Failure-rate by category. Regression catches with $ impact.
Full documentation so your team operates the eval workspace after handoff.
The methodology is only worth what it surfaces on your stack. Book a 30-minute call and we'll tell you what we'd find — and what your first eval bench would look like.
