In this article
A planning loop (also called a reasoning-acting loop) is the engine of agentic behavior. The LLM generates a plan or next action, executes it via a tool, receives the observation, then re-plans based on new information — continuing until the goal is achieved or a stopping condition is met. Variants include: fixed-step planning (plan all steps upfront, then execute), dynamic replanning (replan after each observation), and hierarchical planning (top-level plan with nested sub-plans). Controlling loop termination is critical — runaway agents can loop indefinitely and consume unlimited tokens.
What it means in practice
Planning Loop is not just vocabulary; it is a design handle. In agentic systems, this term usually appears when engineers are designing, reviewing, or troubleshooting real production flows rather than only naming the concept. It shows up when an AI system must choose actions, call tools, remember state, and recover from partial failures.
Why engineers care
- It gives teams a shared name for the behaviour, risk, or architecture choice being discussed.
- It helps separate the goal from the implementation detail, so you can compare alternatives instead of copying a tool pattern blindly.
- It creates a useful checklist for reviews: inputs, outputs, failure modes, ownership, cost, latency, and measurement.
Production watch-outs
Do not add autonomy without observability and limits. Tool permissions, retries, budgets, timeouts, and human review paths should be explicit.
Related context
Useful neighbouring concepts: Agent, React Prompting, Agentic Workflow, Tool USE.