How to Know Whether an AI Agent Actually Finished the Task
Verify final state, side effects, and user intent instead of trusting an agent's confident completion message.
8 minEvalGate Guides
Solve the AI behavior problem in front of you
Start with the failure, question, or decision—not a product category. Each guide gives you a direct answer, a practical method, useful measurements, and the mistakes that make the evidence lie.
Start here
Start with a symptom
These are the questions teams usually ask before they know which evaluation method or tool category they need.
Agent failures
How do I know whether an AI agent really completed a task?
Treat the agent's final message as a claim, not proof. A task is complete only when an independent check confirms the intended final state, required side effects, and important constraints against the exact task the user approved.
8 minRead guide
Agent failures
Why does my AI agent work in a demo but fail in production?
Demos remove variance. Production adds messy intents, long histories, stale data, permission boundaries, concurrent state, partial tool failures, and real latency. Diagnose the gap by comparing complete execution conditions, not just prompts or final answers.
9 minRead guide
Prompt and model changes
How do I test whether a prompt change made my AI application worse?
Freeze the current prompt as the baseline, change only the prompt, and run both versions on the same cases with matched model settings. Inspect per-case regressions and protected slices before looking at the average, then review meaningful output differences.
8 minRead guide
RAG and hallucinations
How do I measure whether a RAG answer is faithful to its sources?
Measure faithfulness by decomposing the answer into material claims and checking whether each claim is entailed by the context actually retrieved for that run. Score unsupported, contradicted, and unverifiable claims separately; a relevant answer can still be unfaithful.
9 minRead guide
Tools and trajectories
How do I test an MCP server before shipping it?
Test protocol negotiation and discovery first, then every tool's schema, authorization, tenant boundary, success behavior, failure semantics, and side effects. Finish with a compatibility matrix using real client implementations and an independently verified result.
10 minRead guide
Production quality
How do I monitor the quality of an AI application in production?
Capture enough trace and outcome evidence to reconstruct important interactions, then evaluate a risk-weighted sample plus every critical trigger. Track failure topics and protected slices over time, route uncertain high-impact cases to review, and convert confirmed escapes into regression tests.
10 minRead guide
Answer library
Browse all 42 guides
Agent failures
Diagnose agents that look healthy in a demo but fail, loop, stall, or claim work they did not finish.
How to Debug an AI Agent That Calls the Wrong Tool
Find whether wrong-tool failures come from tool descriptions, routing, context, arguments, permissions, or recovery logic.
9 minHow to Stop an AI Agent from Looping or Repeating Tool Calls
Detect non-progress, cap retries, preserve idempotency, and stop repeated calls before they burn budget or duplicate work.
8 minWhy an AI Agent Works in a Demo but Fails in Production
Find the hidden differences in inputs, state, permissions, tools, load, and success criteria that make demos misleading.
9 minHow to Debug a Slow AI Agent Without Sacrificing Quality
Locate latency in model, retrieval, tools, queues, retries, or orchestration and optimize against verified outcomes.
8 minHow to Investigate AI Agent Failures Without Guessing
Use a repeatable triage order to separate intent, context, retrieval, tool, orchestration, policy, and outcome failures.
10 minEvaluation foundations
Define quality, build representative cases, choose graders, and create evaluation evidence your team can trust.
How to Test an LLM Application Before Production
Build a representative test set, choose the right graders, and gate releases on behavior instead of demos.
9 minHow to Build an AI Evaluation Dataset That Represents Real Use
Turn requirements, real examples, edge cases, and production failures into a reviewable evaluation set.
9 minHow to Choose LLM Evaluation Metrics That Drive Decisions
Match metrics to product behavior, risk, and release decisions instead of collecting scores that look scientific.
7 minCode-Based Evals vs. LLM Judges: Which Should You Use?
Choose deterministic checks, semantic judges, or a layered combination based on what can be verified.
8 minHuman Review vs. LLM Judges for AI Evaluation
Decide what people should label, what models can grade, and where disagreement must stop automation.
7 minHow to Calibrate an LLM Judge Before Trusting Its Scores
Measure false passes, false failures, stability, and drift against a reviewed human anchor set.
9 minPrompt and model changes
Compare prompts, models, routes, and releases without letting a better average hide a critical regression.
How to Test a Prompt Change Before Deployment
Compare the changed prompt with a frozen baseline across representative, protected, and adversarial cases.
8 minHow to Compare LLM Models for Your Application
Choose a model with application-specific quality, latency, cost, and reliability evidence instead of benchmark rank.
9 minHow to Catch LLM Regressions in CI
Run stable fast checks on pull requests and reserve heavier semantic evaluation for the right release stage.
9 minHow to Set Pass/Fail Thresholds for AI Evaluations
Turn quality scores into stable release decisions with risk tiers, uncertainty bands, and protected slices.
7 minHow to Test LLM Routing and Fallback Logic
Verify that model routers choose the right path, degrade safely, and preserve quality during provider failures.
8 minHow to Turn Production AI Failures into Regression Tests
Preserve a failed interaction, remove sensitive data, confirm the expectation, and add durable coverage.
9 minRAG and hallucinations
Separate retrieval failures from generation failures and test grounding, citations, and knowledge changes.
How to Measure RAG Answer Faithfulness
Check whether each material claim is supported by retrieved evidence without confusing relevance with grounding.
9 minHow to Tell Whether a RAG Failure Came from Retrieval or Generation
Use staged checks to find whether the right evidence was missing, buried, ignored, or misrepresented.
8 minHow to Test RAG Chunking Changes Without Breaking Answers
Compare chunk size, overlap, boundaries, metadata, and ranking on retrieval and answer outcomes.
7 minHow to Test AI Citations and Source Attribution
Verify that cited sources exist, support the nearby claim, match the retrieved version, and remain accessible.
8 minHow to Reduce Chatbot Hallucinations with Evaluation
Find hallucination patterns, improve evidence and abstention, and prevent known failures from returning.
9 minHow to Build a Golden Dataset for RAG Evaluation
Create versioned queries, required evidence, acceptable answers, and failure labels for retrieval and generation.
8 minTools and trajectories
Evaluate tool selection, multi-step execution, MCP behavior, approvals, handoffs, and final state.
How to Evaluate a Multi-Step AI Agent
Test planning, tools, recovery, state, and completion across the full task instead of grading only the final answer.
9 minHow to Test AI Agent Tool Calls
Verify selection, arguments, permissions, results, retries, abstention, and side effects with separate checks.
8 minHow to Test an MCP Server Before Release
Go beyond a successful handshake to test discovery, schemas, authorization, isolation, failures, and real clients.
10 minHow to Compare Two AI Agent Trajectories
Find the first meaningful divergence in plans, tools, evidence, retries, costs, and outcomes.
8 minHow to Test Human Approval in an AI Agent Workflow
Prove that approvals bind to an exact action, stale decisions fail closed, and side effects cannot happen early.
9 minHow to Evaluate Multi-Agent Handoffs
Test routing, context transfer, ownership, duplicated work, and end-to-end outcome across collaborating agents.
9 minProduction quality
Turn live behavior into useful signals while controlling cost, latency, nondeterminism, and alert volume.
How to Monitor AI Quality in Production
Connect sampled interactions, quality scores, failure topics, traces, and user outcomes without grading everything blindly.
10 minHow to Choose Production Traces for Evaluation
Build a representative, risk-aware sample without overfitting to easy traffic or noisy complaints.
8 minHow to Measure AI Agent Task Success Rate
Define a denominator, verify outcomes, preserve partial completion, and connect quality to business results.
8 minHow to Reduce LLM Evaluation Cost Without Creating Blind Spots
Layer deterministic checks, targeted sampling, caching, routing, and staged suites while preserving critical coverage.
9 minHow to Balance AI Quality, Latency, and Cost
Choose from the quality-qualified Pareto frontier instead of hiding tradeoffs in one weighted score.
8 minHow to Fix Flaky and Nondeterministic AI Evaluations
Separate application variance, grader variance, infrastructure failure, and threshold sensitivity before adding retries.
9 minSafety and governance
Test prompt injection, privacy, permissions, red-team coverage, and release controls before an incident.
How to Test Prompt Injection Defenses
Evaluate instruction hierarchy, untrusted content, tool permissions, data access, and safe recovery across realistic attacks.
10 minHow to Build AI Red-Team Test Cases Before Launch
Turn assets, threat paths, controls, and expected outcomes into reviewed adversarial coverage.
9 minHow to Test an AI Application for PII Leakage
Probe memorization, retrieval, tool results, logs, cross-tenant access, and output redaction without putting real people at risk.
9 minHow to Create an AI Release Gate
Turn evaluation evidence into a repeatable pass, review, or block decision tied to the exact release.
10 minHow to Audit AI Agent Permissions and Tool Access
Inventory credentials, scopes, tools, targets, approval boundaries, and actual use before granting more autonomy.
9 minHow to Run Continuous AI Evaluation Without Alert Fatigue
Design risk-aware sampling, grouped incidents, evidence-rich alerts, and bounded remediation instead of noisy score alarms.
9 min