Reasoning, Planning, and Memory
Explore the fundamentals of multi-step AI agents by understanding how they manage reasoning, planning, and memory. This lesson helps you learn to track state changes and implement stop conditions to prevent infinite loops. You will discover techniques for balancing short-term state and persistent memory to improve agent performance and reliability.
A multi-step agent does not return one answer. It emits a sequence of intermediate messages, tool calls, and state updates, and the final response is only the last artifact in that sequence.
The Research Assistant loop below is representative. Notice the visible symptom. Retrieval gets called repeatedly, but the state does not change in a way that would make a different tool choice likely.
The diagram below illustrates how goal, tool, args, and internal state fields mutate across each step of an agent execution trace.
What makes an infinite loop detectable is that each step logs inputs, tool calls, and state transitions. If facts gains no new information and todo remains unchanged after a tool run, ...