While this is true, there is also a layer in the harness between the output of _any_ tool output (eg stdout or hand-rolled tools) and the LLM. A tool could read the file but then the agentic harness could redact the output before returning it back to the llm if any of the contents matched the file contents. We do something similar in Plotly Studio where we check the entropy of strings in the user input and flag & redact any high entropy strings to the user as “potential credentials” thay the user might have inadvertently copied and pasted into the prompt before sending to the llm.
There are ways around this - the llm can always be clever by invoking tools to read the file contents in a different way than the direct file contents - but this is all to say that the agentic harness layer _does_ allow for deterministic logic in between tool output and the LLM requests.
This is really cool. And timely! Check out the recent paper by Google et al re "Societies of Thought": https://arxiv.org/html/2601.10825v1. It goes into how different conversational behaviors (raising questions or just say "but wait..."), perspective shifts, conflict of perspectives, tension, tension release (jokes!), asking for opinions) and different personalities (planner, expert, verifier, pragmatist) is both a sign of and can result in much higher performance reasoning.
So I'd be curious to see if encouraging certain conversational behaviors might actually improve the reasoning and maybe even drive towards consensus.
There are ways around this - the llm can always be clever by invoking tools to read the file contents in a different way than the direct file contents - but this is all to say that the agentic harness layer _does_ allow for deterministic logic in between tool output and the LLM requests.