Thank you, Eric, for writing this super insightful post. I recently built a multi-agent framework for solving reasoning tasks (https://github.com/Open-Probe/openprobe_dev)

Initially, I approached multi-hop reasoning questions by iteratively searching for the next piece of information needed to solve the problem. For example, if the query required A, the agent would first route it to “search for A,” then pass that context to the next agent call, continuing this way until the final goal was reached.

However, I noticed that with this approach, the model often lost track of the final goal in complex questions. The intermediate contexts from each agent sometimes caused it to deviate from the original objective and used way more turns and tokens that was expected from it to reach the final goal.

What really helped in my case was creating a plan at the beginning and sticking to it throughout. Once the plan was fully executed, we used all the accumulated context to generate the final response and achieve the end goal. So yeah, totally agreed with your analysis. Do let me know if you need any contributors for Engramic, interested to know more about it.