Export a Runnable Eval Suite Before the Read-Only Date
Treat the read-only date as the export deadline: a runnable suite needs a completed run and rebuilt graders.
Evals
Add Three Trace Checks So Users Trust Agents
Task completion alone will not earn trust, so add trace-level checks for unauthorized actions, false attribution, and deceptive completion before launch.
Fork-style evals for long-running AI agents
Stop trusting final task success. Mine real agent trajectories into fork cards that test the intermediate choices users will actually feel.
Four-channel leak audit: make pass rates mean capability
Audit your AI evals for hidden answers, broken tasks, and grader drift before you use a pass rate as a ship signal.
Score agent evals so guardrail violations don't hide
Zero guardrail-violating tasks, split completion from clean completion, and put cost per task on the same card before you choose a model.
Make agent evals survive the next rule change
Audit agent evals for business understanding, operational execution, and learning persistence; releases survive new cases, elapsed time, and rule changes.
Ship an AI Feature, Not Just a Demo, With Coding Agents
Grade the finished agent on unseen end-to-end tasks, then audit the build for shallow research, too few client questions, and unhandled constraints.
How to log AI benchmark scores so model comparisons survive an index change
Record index version, model version, date, score, and test-mix changes so comparisons stay comparable when the benchmark ruler changes.
Ship model updates without breaking trust
Treat model versions like code changes: diff behavior, classify the delta, and gate promotion on user-visible regressions.
Ship AI Updates: A 160-Case Model-Prompt Regression Suite
Build a small, product-shaped eval suite that separates model changes from prompt changes before users feel the difference.
Build a CI gate that catches agent tool failures before users do
Treat broken tools as test fixtures, replay golden traces, and block merges when recovery behavior fails.
Make AI-Generated Code Safe Enough to Ship: A Review Gate for Agent PRs
A five-point gate for agent pull requests: provenance, diff scope, regression coverage, format stability, and divergence accounting before merge.
Design evals that catch agent steps, not outputs
When an agent's value depends on the path it takes, output-only checks can miss the exact rule violation that erodes user trust.
Grade every agent thread without breaking the eval budget
A specialist judge can make full-conversation evaluation cheaper than frontier LLM-as-judge sampling, if the error taxonomy matches your trust failures.
Ship Prompt Changes Like Code With a Small Eval Suite
Treat prompt edits as code changes: version the cases, score each task, and block merges when a small suite shows a regression.
AI JSON, Forms, and Extraction: A Four-Gate Trust Checklist
A better prompt will not make structured output safe; use four gates: schema validity, field-level accuracy, confidence calibration, and a fallback that keeps users out of trouble.
Make Your RAG Answer Defensible: A Checklist That Shows Its Work
A trustworthy RAG answer shows its evidence, stays faithful to it, cites the exact claim, and refuses when the evidence is missing.
AI Support Scorecard for Unresolved, Unsafe, and Expensive Tickets
Use resolved-without-escalation, safe handoff, factual accuracy, and cost per ticket to decide when an AI support agent can take more traffic.
Audit your AI prediction feature with a four-test odds benchmark
A trustworthy prediction feature treats odds as calibrated probabilities, proves them with backtests, explains the basis, and limits gambling harm.
Audit Your AI Health Assistant’s E. Coli Answer: No Diagnosis, Clear Uncertainty, Citations, and Escalation
A consumer health chatbot should treat an acute-infection question as a safety event, not a symptom checker.
Ship Small Models Only After Trust Evals
A fast training claim is not a product; it is a hypothesis. Run workflow-level evals before users meet the model.
Ship agent evals that catch task failures, safety slips, cost spikes, and latency regressions before users do
Treat agent evals as a design system: instrument sessions, score task success, safety, cost, latency, and feedback, then gate releases before users meet regressions.
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