What is Agent Planning?

Imagine you want your LLM agent to do something more complex than just answer a single question or perform one isolated action. What if you need it to, say, research a topic from several web pages, summarize the findings, and then draft an email with that summary? This isn't a single leap, it's a sequence of steps. This is where agent planning comes into play.

Agent planning is essentially the process an LLM agent uses to figure out a sequence of actions to achieve a specific goal. Instead of just reacting to an immediate instruction, an agent with planning capabilities can look ahead, determine the necessary steps, and decide the order in which to perform them. It’s like creating a to-do list or a roadmap before embarking on a task.

Why is Planning Important for LLM Agents?

Without planning, an LLM agent is largely limited to single-turn interactions or very simple, predefined sequences. Planning unlocks the ability to tackle more sophisticated tasks:

1. Completing Multi-Step Objectives: Many real-world tasks require several actions performed in a logical order. For instance, booking a vacation might involve searching for flights, then hotels, then rental cars, and finally confirming all bookings. Planning allows an agent to manage such multi-step processes.

2. Intelligent Tool Usage: As we saw in the previous chapter, tools extend an agent's capabilities. Planning helps the agent decide not just if a tool is needed, but which tool to use, when to use it, and what information to pass to it, potentially using the output of one tool as input for another.

3. Task Decomposition: Complex goals can be overwhelming. Planning allows an agent to break down a large, daunting objective into smaller, more manageable sub-tasks. This makes the overall problem easier to solve.

Think about how you might approach a task like assembling flat-pack furniture. You wouldn't just grab random pieces and start screwing them together. You'd look at the instructions (the plan), identify the first step, perform it, then move to the next, and so on. An LLM agent, through planning, attempts a similar, albeit simpler, methodical approach. The LLM itself, with its reasoning abilities, plays a central role in formulating this plan, often by "thinking" through the steps required.

Core Ideas in Agent Planning

At its heart, agent planning involves a few fundamental ideas:

• A Clear Goal: The agent needs a well-defined objective. What does "success" look like for this task? This is the destination on its roadmap.
• Available Actions: What can the agent actually do? These are its tools, its ability to call the LLM for reasoning, or other functions it can execute.
• Understanding the Current Situation: The agent often needs to consider the current state of affairs or information it has gathered so far to make informed decisions about the next step. This involves observation and memory.
• A Strategy for Choosing Actions: The agent needs a way to select the most appropriate next action from its available options to move closer to the goal. This strategy is what this chapter will explore through techniques like Chain-of-Thought and ReAct.

For example, if an agent's goal is to "provide a summary of today's news on renewable energy," its plan might involve:

1. Access a news search tool with the query "renewable energy news today."
2. Receive a list of articles.
3. For each relevant article (or a few top ones): a. Use a web browsing tool to fetch the article's content. b. Use the LLM to summarize the content.
4. Combine the summaries into a coherent overview.
5. Present the final summary.

This sequence is a plan. Each step is an action, and the order matters.

Visualizing the Basic Planning Process

We can visualize this flow at a high level. The agent starts with a goal, goes through a planning phase (often using the LLM's reasoning), and then executes a sequence of actions, observing the results along the way.

A simplified flow showing how an agent might move from a goal, through planning and action execution, towards achieving that goal. The planning process itself might be iterative, refining the plan based on new observations.

This chapter will focus on basic planning techniques. More advanced AI planning can involve sophisticated algorithms for searching through many possible sequences of actions, adapting to highly dynamic environments, and learning from past experiences. However, understanding the foundational approaches we'll cover here is an important first step.

By grasping what planning is and why it’s a necessary ingredient for more capable agents, you're ready to look at how these agents can begin to formulate and execute simple plans. We'll start by examining how guiding an agent's reasoning process can itself be a form of planning.