I feel like one small piece is missing to call it an agent? The ability to iterate in multiple steps until it feels like it's "done". What is the canonical way to do that? I suspect that implementing that in the wrong way could make it spiral.
When a tool call completes the result is sent back to the LLM to decide what to do next, that's where it can decide to go do other stuff before returning a final answer. Sometimes people use structured outputs or tool calls to explicitly have the LLM decide when it's done, or allow it to send intermediate messages for logging to the user. But the simple loop there lets the LLM do plenty of it has good tools.
So it returns a tool call for "continue" every time it wants to continue working? Do people implement this in different ways? It would be nice what method it has been trained on if any.
The model will quickly stop tool calling on its own; in fact, I've had more trouble getting GPT5 to tool call enough. The "real" loop is driven, at each iteration, by a prompt from the "user" (which might be human or might be human-mediated code that keeps supplying new prompts).
In my personal agent, I have a system prompt that tells the model to generate responses (after absorbing tool responses) with <1>...</1> <2>...</2> <3>...</3> delimited suggestions for next steps; my TUI presents those, parsed out of the output, as a selector, which is how I drive it.
