Where to go from here

This workshop covers building a coding agent with tool calls, a skills system, and two framework implementations. Several important topics were intentionally left out.

Evaluation

We did not measure how well the agent performs.

A real coding agent needs evaluations with:

• a task set
• an automated runner
• metrics that tell you whether a change to the prompt or tools made things better or worse

Without evaluation, every prompt tweak is a guess.

Multi-agent patterns

The agent in this workshop is a single loop with one set of tools. More complex setups use multiple agents that specialize and collaborate. One agent plans, another writes code, and a third reviews it. That pattern adds power but also adds coordination complexity.

Deployment

We ran the agent in a notebook. Deploying it as a production service is a separate problem. You need an API, authentication, rate limiting, and persistent history. See the end-to-end agent deployment workshop for one approach.

MCP integration

The Model Context Protocol (MCP) is a standard for exposing tools to agents. The skills system we built is a simple version of this idea. MCP provides a more general protocol with discovery, schema negotiation, and tool composition. Integrating MCP would let the agent use tools from any MCP-compatible server.

Other frameworks

We used ToyAIKit and PydanticAI. The same patterns apply to other agent frameworks such as LangGraph, CrewAI, AutoGen, and others. The important part is understanding the tool-call loop - the framework is packaging around that core idea.