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 starting a task.
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:
Completing Multi-Step Objectives: Many 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.
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.
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.
Fundamentally, agent planning involves a few fundamental ideas:
For example, if an agent's goal is to "provide a summary of today's news on renewable energy," its plan might involve:
This sequence is a plan. Each step is an action, and the order matters.
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.
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