2 posts tagged with "agent-skill"

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.

Our most important practical finding today was simple:

The shorter and clearer the prompt, the more effectively the coding agent works.

That does not mean removing constraints. It means moving detailed constraints out of prose and into an executable harness: the TeaQL Generation Service, generated typed APIs, model-aware assist, compiler feedback, and tests.

Today we rebuilt TeaQL Agent Kit around that finding. The agent creates a typed domain contract, the Generation Service evaluates it, generated guidance constrains the implementation, and human review happens in parallel.

The smaller, clearer workflow is now distributed as a standard Agent Skill named build-teaql-app.