Video

Agent Harness explained in 8min..

Caleb Writes Code, YouTube.May 23, 2026

Description

Watch: full video.

This short explainer is really about the next layer of AI work: not better prompts alone, and not just better context, but the harness around the model that decides how tasks are broken down, repeated, checked, and continued. The useful distinction is that prompt engineering controls how you ask, context engineering controls what the model can see, and harness engineering controls the working system around the agent. For knowledge workers, the practical lesson is that reliable AI work depends less on a single clever instruction and more on the surrounding loop: task state, checkpoints, verification, and human judgement.

Official video embed

Caleb Writes Code, YouTube

This video is hosted by a third-party provider. It will load only after you choose to press Play.

This video can load through the original platform's official player where available.

Open original source

Key Takeaways

The important shift is from prompting the model to designing the system around it. As AI tasks become longer and more complex, the quality of the surrounding workflow can matter as much as the model itself.
Long-context work has a memory problem. When an agent keeps summarising its own progress, details can get distorted, so external state, files, task lists, and checkpoints become more reliable than conversation history alone.
A good harness makes the agent work in loops. It breaks a large task into smaller rounds, gives each round fresh context, records what changed, tests the result, and then decides what should happen next.
The practical question is not “which model is this using?” but “how does this system manage work?” The useful evaluation points are task decomposition, state persistence, verification, error recovery, and where a human stays in the loop.