Reasoning, Planning, and Memory
Explore how multi-step AI agents orchestrate reasoning, planning, and memory to manage tool use effectively. Understand detecting infinite loops, creating executable plans with stop conditions, and differentiating short-term state from persisted memory. Learn to enforce budgets and evaluate progress to maintain reliable agent operations and reduce latency while improving output quality.
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 ...