Lightweight Skills, Strong Tools: A Practical Architecture for Reliable AI Agents
As AI agents become more capable, it is tempting to make their Skills increasingly detailed. Every new failure becomes another instruction. Every exception becomes another paragraph. Eventually, the Skill tries to describe the domain, encode the workflow, enforce safety, teach the tool interface, and anticipate every future mistake.
That approach does not scale.
Our practical conclusion is different:
Keep Skills lightweight. Make Tools strong.
This is not an argument for fewer safeguards. It is an argument about where safeguards belong.
It is also a principle for harness engineering. The core of an agent harness should not be an ever-growing library of Skills. It should be a set of strong, model-aware Tools and deterministic feedback loops that turn guidance into enforceable action boundaries.