This reflects my experience. Yet, I feel that getting reliability out of LLM calls with a while-loop harness is elusive.

For example

- how can I reliably have a decision block to end the loop (or keep it running)?

- how can I reliably call tools with the right schema?

- how can I reliably summarize context / excise noise from the conversation?

Perhaps, as the models get better, they'll approach some threshold where my worries just go away. However, I can't quantify that threshold myself and that leaves a cloud of uncertainty hanging over any agentic loops I build.

Perhaps I should accept that it's a feature and not a bug? :)

Forgot to address the easiest part:

> - how can I reliably call tools with the right schema?

This is typically done by enabling strict mode for tool calling which is a hermetic solution. Makes llm unable to generate tokens that would violate the schema. (I.e. LLM samples tokens only from the subset of tokens that lead to valid schema generation.)

Re (1) use a TODOs system like Claude code.

Re (2) also fairly easy! It's just a summarization prompt. E.g. this is the one we use in our agent: https://github.com/HolmesGPT/holmesgpt/blob/62c3898e4efae69b...

Or just use the Claude Code SDK that does this all for you! (You can also use various provider-specific features for 2 like automatic compaction on OpenAI responses endpoint.)