An Agent Control Loop Is More Useful When You Can Explain Every Turn
Most coding-agent demos leave out the part that determines whether the system will be maintainable: the control loop.
An answer may look simple—prompt, model call, tool call, result—but an implementation must also decide how streamed model events become stable messages, how a tool result is paired with the call that produced it, how a session resumes, and what survives context compaction.
pi-textbook is useful as a learning artifact because its public README organizes a Pi-style coding agent into 15 checkpoints. The stated path moves through a TypeScript protocol, streaming model events, a provider, tools, an agent loop, a session tree, context compaction, extensions, and evals.
Top comments (1)
Explainable turns are underrated. A control loop that only stores the final answer loses the useful part: why this tool, why this state change, why this retry, and what evidence justified the next step.
I would keep the explanation tied to events, not prose after the fact. If every turn has inputs, decision, tool call, result, and confidence, debugging becomes much less theatrical.