Real failures, practical testing guides, and lessons learned from shipping autonomous AI into production.
Answer 10 questions about your agent's security, reliability, and safety controls. Get an instant readiness score (0–100) plus a personalized fix guide for every gap you find.
Head-to-head benchmark comparison of 10 AI coding tools. Real SWE-bench and HumanEval scores for Claude Code, Cursor, Copilot, Devin, and more — including the 3 tools every other listicle misses.
Most teams running AI agent benchmarks are measuring something — but not what they think. Learn the 5 signal dimensions, what benchmarks miss, and how to build one that catches real failures.
Hallucination, context drift, capability regression, tool call drift, output format drift, persona drift, and edge case failures. A practical breakdown with examples and detection methods for each.
From deleted production databases to $172M lawsuits, real AI agent failures that cost companies billions. Every single one was preventable with proper behavioral testing before launch.
A practical step-by-step guide to testing AI agents end-to-end before they touch real users. Covers injection resistance, hallucination detection, consistency checks, and escalation behavior.
Monitoring tells you what's happening. Validation tells you if your agent is actually safe. Here's why you need both, and how to build a QA layer that catches failures before your users do.
15 essential verification items covering security, reliability, consistency, and behavioral boundaries. Use this pre-launch checklist to catch 90% of production failures before they happen.
AI agents face attacks that traditional apps never see. This guide covers the 3 attack surfaces — prompt injection, data exfiltration, unauthorized tool use — with real examples, test patterns, and a 10-item security checklist.
Hallucinations are the #1 reliability risk in production AI agents. Learn the 4 types of LLM hallucinations, a 3-technique testing methodology, and an 8-item test suite that catches false outputs before users do.
AI agents degrade silently. Learn how to detect behavioral drift, capability loss, and safety boundary erosion with automated regression testing — before users do.
AI agent response times are non-deterministic and hard to debug. Learn to measure TTFT, total latency, token throughput, and tool call overhead — with practical benchmark methodology and SLA definitions for production AI.
47 test cases covering injection, hallucination, consistency, and boundary violations — with scoring rubrics and a production deployment checklist.
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