The Rise of Harness Engineering
Harness Engineering has become the defining conversation in AI agent development this quarter. Anthropic published "Effective Harnesses for Long-Running Agents." OpenAI released their own take on constraining agent behavior through software engineering practices. The thesis is straightforward: wrap your AI agent in a structured control layer—task routing, approval gates, verification loops, and retrospectives—so it behaves reliably over extended sessions.
The pattern makes intuitive sense. An unconstrained agent is a liability. A harnessed agent is a tool. The community has responded: open-source harness frameworks are emerging, giving teams reusable scaffolding for decision-level reliability.
But here's the question no one is asking loudly enough: after the harness decides what to do, how does the agent actually do it?
What Harness Solves
