Why Do AI Evals Pass While Users Still Fail?
A reproducible fixture shows how a polished handoff can pass deterministic and LLM grading while a user still has to correct the work.
Topic collection
AI evaluation
Test quality, reliability, safety, observability, and production outcomes.
How to Compare AI Models on Your Own Business Tasks
Compare AI models on representative business tasks with a reusable scorecard, hard vetoes, human checks, and cost and latency evidence.
How to Measure Whether AI Coding Saves Time on a Small Team
A reproducible small-team protocol for measuring AI coding time, review, rework, tests, and accepted changes without inventing a productivity result.
How to Tell Whether a RAG Failure Is Retrieval or Generation
A reproducible paired-context test for finding out whether a wrong RAG answer came from missing evidence or bad use of available evidence.
How to Test an AI Feature Before Production Data
Build a small authored test set, run it locally, reject critical failures, and keep synthetic evidence separate from what only production can prove.
Why Do AI-Generated Summaries Miss the Decision That Matters?
A public-record failure test shows why polished AI summaries lose rationale, dissent, ownership, and uncertainty, then gives you a repair contract.
How to Build an Evaluation Dataset From Production Traces
Turn noisy production traces into a private, replayable evaluation dataset with verified outcomes, deliberate sampling, and stable versioning.
How to Measure AI Output Quality When There Is No Single Right Answer
A practical method for measuring open-ended AI output with rubrics, pairwise review, human calibration, and outcome-based release gates.
How to Write Acceptance Criteria for an AI Feature
Write AI feature acceptance criteria around observable outcomes, evidence, boundaries, and a clear non-success path your team can test.
How to Replay a Failed Multi-Agent Workflow Safely
A practical recovery plan for replaying failed multi-agent workflows from durable checkpoints without repeating actions or hiding uncertainty.
Can an AI Agent Safely Triage Support Tickets?
AI can classify and route low-risk tickets, but safe triage needs a narrow action boundary, untrusted-input controls, and human review for exceptions.
Why Does My AI Agent Give a Different Answer Every Time?
Diagnose inconsistent AI agent answers by separating sampling drift from changing context, tool results, retrieval, model versions, and unclear acceptance criteria.
How to Design Idempotent Tools for AI Agents
Design idempotent AI-agent tools that survive retries, crashes, and lost responses with keys, fingerprints, durable claims, replayed outcomes, and reconciliation.
How to Handle Rate Limits in an AI Agent
Handle AI agent rate limits with shared admission control, bounded retries, jitter, durable waits, and clear decisions to queue, degrade, or stop.
How to Validate AI Agent Inputs Before a Run
Validate an AI agent run envelope for shape, meaning, authority, policy, and cost before the model or tools see it.
How to Version Prompts for AI Agents in Production
Version prompts like deployable behavior: pin the full runtime identity, test changes, promote by environment, trace every run, and keep rollback one pointer away.
Why Does My AI Agent Ignore Instructions? A Practical Fix
Find out why an AI agent ignores instructions by tracing authority, context, ambiguity, untrusted data, enforcement, and evaluation in order.
How Do I Give an AI Agent Least-Privilege Access to Tools?
A practical way to scope an AI agent’s identity, tools, operations, data, credentials, and time before it can touch a real system.
Why Does My AI Agent Choose the Wrong Tool? A Practical Diagnosis
Find out why an AI agent picks the wrong tool by separating availability, description overlap, schema, runtime constraints, and misleading tool results.
Why Is My AI Agent Stuck in a Loop? Diagnose and Fix It
Diagnose a looping AI agent with trace-first checks, verify real progress, contain runaway work, and add the right retry, recovery, or stop condition.
How to Monitor an AI Agent in Production: An Observability Contract
Monitor production AI agents with a traceable run record, verified outcomes, action and safety signals, privacy controls, and a failure-to-evaluation loop.
How Do I Prevent Prompt Injection in an AI Agent?
A practical, vendor-neutral guide to reducing prompt-injection risk with trust boundaries, least-privilege tools, approval binding, and adversarial tests.
How to Test Multi-Agent Handoffs Before Production
A practical test plan for multi-agent handoffs: check context, artifacts, authority, conflicts, retries, and replay before production.
How to Evaluate an AI Agent: A Practical Release Gate
A vendor-neutral framework for testing agent outcomes, tool use, security, cost, and stability before each release.
Human-in-the-Loop AI Agents: Approval Gates That Work
A vendor-neutral method for deciding which AI agent actions need human approval, binding each decision to the exact action, and testing the gate.