Not all harnesses are created equal.

And there is no one-size-fits-all.

A good harness should solve your context engineering problems.

Understanding and optimizing lower-level details like system prompts, caching, and effective tool calling is how the harness stands out.

This is why harness engineering is an important skill for AI engineers today.

Start by building your own tiny/minimal harness (in your favorite language) with a basic agent loop, system prompt, and tool calling capabilities.

Test it on a use case, verify results, and iterate on it. In no time, you realize that anyone can build a quite powerful harness.

But it's important to understand those lower-level details first. Understand the role they play and how to tune them. These skills can transfer easily to existing, more production-ready harnesses when you get there. At that point, you are better equipped to modify and tune that harness for your tasks.

This is not about just learning fundamentals. It's having a good grasp of the knobs you can tune to get the most out of your agent harnesses.