Brief safely
Sanitized context and one concrete reliability decision.
01Agent evaluation
Turn agent failures into gates your engineering team can inspect.
Agent workflows fail through tool choice, missing context, state drift, unsafe actions, weak escalation paths, and brittle prompt patches. The work focuses on replayable evidence and release criteria, not generic agent enthusiasm.
Reproduce
Turn traces, examples, or eval runs into replayable evidence.
Define gates
Separate blockers, warnings, thresholds, and ownership.
Leave a record
Connect the evidence to a ship, revise, or stop call.
02Scope ledger
What enters the engagement—and what leaves it.
A strong fit
01The agent has tool calls, handoffs, state, or workflow policy that can fail in different ways.
02The team needs to know which failures block release and which require monitoring or revision.
03The current evals do not explain why a candidate version passes or fails.
What your team keeps
01Agent behavior evaluation planReusable within the agreed project scope.
02Replay rows grouped by failure modeReusable within the agreed project scope.
03Tool-use and escalation failure taxonomyReusable within the agreed project scope.
04Blocker/warning release-gate checklistReusable within the agreed project scope.
03Working sequence
From uncertain behavior to an inspectable decision.
The sequence is deliberately legible: agree the boundary, reproduce what matters, then make the release rule explicit.
Identify the agent actions that carry user, business, or operational risk.
Create representative replay rows from sanitized traces or sample tasks.
Classify failures by trigger, expected owner, and release impact.
Define gates that make release review concrete.
04Open method
Inspect the reasoning before you engage.
These notes expose the decision framework behind the work. They are methodology, not disguised client proof.
01Technical noteFailure Taxonomies for Agent WorkflowsHow to group agent failures by trigger, owner, severity, and release impact so evaluation results lead to engineering action.02Technical noteRelease Gates for LLM FeaturesHow to define blockers, warnings, thresholds, and decision records before releasing LLM-backed features.
09Safe first step
Bring the decision your team needs to defend.
Describe the ai agent evaluation and release gates context in sanitized terms. We will use the first exchange to confirm fit, evidence available, and the safest next step.
No credentials, production data, customer records, or private repository access in the first brief.
Prefer to talk it through? Request a 30-minute call