Chain context system

Hi, straight to the point: I’m building an AI agent that operates in a loop. Whenever I ask it a question, it adds the following to the context window: The user’s question System prompts Tool descriptions Previous tool outputs Other conversation state The model then repeatedly calls tools until it decides the task is finished. I’m running into reliability and hallucination issues with two different approaches: **1. Saving the agent’s internal reasoning** The agent generates an internal plan/reasoning step before calling tools, and I save that reasoning into the context for future iterations. This helps maintain continuity, but tokens accumulate very quickly. After a while, the context becomes bloated and the model starts behaving strangely or hallucinating. **2. Not saving the internal reasoning** The agent still generates an internal plan before using tools, but the reasoning is *not* preserved. Instead, only a short summary of the action is stored. This avoids context bloat, but creates another problem: the detailed internal plan is effectively lost after each iteration. As a result, the agent often repeats the same few actions over and over inside the loop, as if it forgets what it already concluded internally. How should I fix this?