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.