Testing AI agents is fundamentally different from testing traditional software. A unit test passes or fails deterministically. An agent evaluation passes or fails probabilistically, because the same input can produce different outputs across runs, and “correct” often requires judgment rather than exact matching. The evaluation tooling landscape has matured in 2026, but choosing between platforms … Read more

Your agent works in development. It passes your test suite. You deploy it to production. Three days later, a customer reports that the agent recommended a product that was discontinued two years ago. Your logs show 200 OK on every API call. Nothing failed. The agent just quietly produced wrong answers while every traditional monitoring … Read more

Every “best AI agent frameworks” article gives you the same thing: a list of frameworks with feature bullets and GitHub star counts. None of them answer the question that actually matters: which one will still work when your agent handles real traffic, real edge cases, and real money? This guide takes a different approach. We … Read more

Most “getting started” tutorials show you how to build an agent. This one shows you how to build an agent that works reliably. The difference is the harness: the verification, cost controls, and error handling that separate a demo from a production system. By the end of this tutorial, you will have a working agent … Read more

LangChain’s own team published a blog post titled “Agent Frameworks, Runtimes, and Harnesses – oh my!” that explains the distinction. Frameworks provide abstractions for building agents. Runtimes provide infrastructure for running them. Harnesses provide opinionated defaults and built-in capabilities for deploying them reliably. LangChain is the first. An agent harness is the third. They are … Read more

Harness engineering builds infrastructure that makes AI agents reliable. Learn the five core components, architecture patterns, and real-world results.