Planning Algorithms Used by AI Agents

Planning is the difference between an agent that flails and one that progresses purposefully toward a goal. Behind an agent's ability to chart a course lie planning techniques, some borrowed from classical artificial intelligence and others native to the language-model era. This article surveys the main planning approaches agents use and the trade-offs that come with each.

What Planning Means for an Agent

For an agent, planning is the process of working out a sequence of actions that leads from the current situation to a desired goal. A plan might be a fixed list of steps decided in advance, or it might emerge incrementally as the agent acts and learns. The aim is the same: to impose enough structure that the agent's actions build toward completion rather than wander.

Different tasks call for different planning styles. A well-understood task with predictable steps can be planned fully in advance, while an uncertain task often requires the agent to adapt its plan as new information arrives. Most real agents blend deliberate planning with on-the-fly adjustment, drawing on whichever techniques fit the moment.

Decomposition and Sequential Planning

The most common planning approach in language-model agents is decomposition: breaking a large goal into smaller subgoals and ordering them sensibly. The agent reasons about what the task requires, lists the steps, and respects the dependencies among them. This produces a sequential plan that the agent then executes step by step, often revising it as results come in.

This style works because language models are good at reasoning about how a task naturally divides. The agent does not need a formal search procedure; it can articulate a plan in plain terms. The weakness is that such plans can be shallow or flawed if the model misjudges the task, which is why pairing decomposition with execution feedback and reflection improves results.

Search-Based Planning

Classical artificial intelligence offers a different lineage: treating planning as search through a space of possible states and actions. Here the agent considers many possible action sequences and looks for one that reaches the goal, exploring options and pruning those that lead nowhere. Techniques in this family systematically expand promising paths and abandon dead ends.

Some agent designs adapt this idea to language models, having the model generate and evaluate multiple candidate steps rather than committing to the first. Tree-of-thought approaches, for instance, let an agent branch into several reasoning paths, assess them, and pursue the most promising. This trades extra computation for a better chance of finding a strong plan, which can matter on hard problems where the first idea is often wrong.

Choosing and Combining Approaches

No single planning method dominates. Sequential decomposition is fast, intuitive, and sufficient for many tasks, while search-based exploration is more thorough but costlier in time and tokens. The right choice depends on how complex the task is, how much a mistake costs, and how much compute is available. Many systems start simple and escalate to heavier planning only when needed.

In practice, planning rarely stands alone. It works alongside execution, memory, and reflection, with the agent planning, acting, observing, and replanning in a continuous cycle. The most robust agents treat their plans as provisional, ready to revise when reality diverges from expectation. Good planning, in the end, is less about finding a perfect plan up front and more about steering effectively as the task unfolds.