For a typical coding agent, there are intermediate tool call outputs and LLM commentary produced while it works on a task and passed to the LLM as context for follow up requests. (Hence the term agent: it is an LLM call in a loop.) You can easily see this with e.g. Claude Code, as it keeps track of how much space is left in the context and requires "context compaction" after the context gradually fills up over the course of a session.

In this regard, the reasoning trace of an agent is trivially accessible to clients, unlike the reasoning trace of an individual LLM API call; it's a higher level of abstraction. Indeed, I implemented an agent just the other day which took advantage of this. The OP that you originally replied to was discussing an agentic coding process, not an individual LLM API call.

Well, right, I see those reasoning stages in reasoning models with Ollama and if you ask it what its reasoning was after the fact what it says is different than what it said at the time.