You deploy an agent. It passes your manual tests. It handles the demo beautifully. Then a customer triggers an edge case where the agent calls the wrong tool, processes the malformed response without noticing, and confidently delivers a wrong answer. No error. No escalation. Just a silent failure that nobody catches until the customer complains. … Read more

A peer-reviewed study running 9,649 experiments concluded that the quality of context you feed a model matters more than the quality of your prompts. That finding is reshaping how production AI teams work. Prompt engineering optimizes what you ask. Context engineering optimizes what the model sees. And for AI agents running multi-step workflows in production, … Read more

OpenAI’s Codex team shipped a million-line codebase with seven engineers in roughly one-tenth the traditional timeline. They did not write the code. They built the harness: the environments, constraints, feedback loops, and verification systems that enabled AI agents to write reliable code. Their philosophy: “Humans steer. Agents execute.” This is harness engineering, and it is … Read more

A team spends three weeks refining their agent’s prompt. They tune the system message, adjust the temperature, add few-shot examples, and iterate on the instruction phrasing. Their task completion rate moves from 85% to 88%. Then a different team adds a structured verification step after each tool call, a pattern that takes two days to … Read more

You have read about ReAct, Plan-and-Execute, multi-agent orchestration, routing, reflection, and a dozen other agent design patterns. Every framework tutorial presents its pattern as the obvious choice. Every architecture blog makes the selected approach look inevitable. And you still do not know which pattern to use for the system you need to build next week. … Read more

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