Ask HN: Why do AI coding agents refuse to save their own observations?

I've spent months building tooling for AI coding agents and hit something I can't fully explain.

If you give an agent (Claude Code, Cursor, Codex) a tool to save observations — "save_observation: persist this insight for future sessions" — and explicitly instruct it to use the tool in system prompts, config files, everywhere you can, it calls it maybe 30% of the time.

The agent will happily use tools that help it complete the current task. But a tool that only benefits future sessions? Almost never.

My working theory: these models are optimized for task completion within the current context window. Saving an observation has zero value for the current task — it's a token cost with no immediate reward. The model has learned that every token spent on "let me save this for later" is a token not spent on the actual work. The incentive structure is wrong at the training level.

I ended up building a passive observation system that watches what the agent does and infers observations from tool calls and AST-level code diffs, without requiring agent cooperation. But I'm curious if others have found ways to make agents reliably self-document.

Has anyone solved this? Techniques like: - Prompt structures that actually get agents to save context - Fine-tuning approaches that reward knowledge retention - Alternative architectures for persistent agent memory

Or is passive observation the only reliable path when the agent won't cooperate?