The best practices for building predictable AI agents are changing
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My first attempt at building an AI agent rested on a simple assumption: give the model enough context and it’ll get the job done.
Anyone who’s built a production AI agent knows that just doesn’t do it. Prompts grow, edge cases pile up, and there’s just too much unpredictability. It’s naive to rely on prompts alone.
Then came tool calls, memory, and evals.
These got us closer.
Tool calls gave us agents interacting with the real world. Memory lets them accumulate state. Evals helped us measure when things went wrong instead of guessing.
Then MCPs.
Even closer, but not quite there yet.
The launch of Skills
Anthropic launched Skills back in October, and I was immediately excited about the benefits of providing an AI agent with procedural knowledge coupled with progressive disclosure.
In Anthropic’s words, Skills were a way to get Claude to perform specific tasks really well by bundling instructions…