To accomplish more than a single, direct response, an LLM agent requires a method for figuring out a sequence of actions. This method is often called "thinking before acting," or basic planning. Rather than simply reacting to immediate input, an agent with planning capabilities formulates a sequence of steps to achieve a more complex objective.The LLM as the "Thinker"At the foundation of this planning process is typically the Large Language Model itself. You've learned that the LLM is the agent's cognitive engine; here, it acts as a rudimentary planner. Given a goal (from your instructions) and a set of available tools, the LLM can reason about:What needs to be done first?What information is required for the next step?Which tool, if any, is appropriate for a particular step?In what order should these steps be executed?This "thinking" doesn't necessarily involve a complicated, separate planning algorithm in basic agents. Often, it's a result of careful prompting that encourages the LLM to outline steps or make decisions about the next action. For example, you might instruct the LLM to "think step-by-step" or "formulate a plan" before choosing an action.From Goals to Actionable StepsMany tasks are too complex to be solved in a single action. Imagine asking an agent to "plan a weekend trip to a nearby city." A simple, one-shot response wouldn't be very helpful. Instead, the agent needs to break this down. This process is often called task decomposition.The agent, guided by the LLM, might break the "plan a weekend trip" goal into smaller, manageable sub-tasks:Identify interesting nearby cities.Check for events or attractions in those cities for the specified dates.Find accommodation options.Suggest transportation methods.Compile a potential itinerary.Each of these sub-tasks might then involve using a tool (like a search engine or a mapping service) or further reasoning by the LLM. The ability to form such a sequence is a fundamental aspect of agent intelligence. It transforms the LLM from a generator of text into an orchestrator of actions.The following diagram illustrates how an agent might break down a high-level goal into a series of smaller, more manageable tasks.digraph G { rankdir=TB; graph [fontname="sans-serif", fontsize=10, bgcolor="transparent"]; node [shape=box, style="filled", fontname="sans-serif", fontsize=10, color="#495057"]; edge [fontname="sans-serif", fontsize=9, color="#495057"]; Goal [label="User's Overall Goal:\n'Plan my team's virtual lunch next Friday.\nOrder from 'Pizza Place' or 'Sushi Spot'.\nBudget: $20 per person for 5 people.'", fillcolor="#a5d8ff"]; PlanGeneration [label="Agent's Initial Thought (Planning):\nLLM breaks down the goal into steps", fillcolor="#ffec99"]; Task1 [label="Sub-Task 1:\nCheck 'Pizza Place' menu & prices.\n(Use web search or API tool)", fillcolor="#b2f2bb"]; Task2 [label="Sub-Task 2:\nCheck 'Sushi Spot' menu & prices.\n(Use web search or API tool)", fillcolor="#b2f2bb"]; Task3 [label="Sub-Task 3:\nCalculate total cost for 5 people\nfor suitable options from each place.", fillcolor="#ffe066"]; Task4 [label="Sub-Task 4:\nCompare options against budget ($100 total).\nSelect feasible options.", fillcolor="#ffe066"]; Task5 [label="Sub-Task 5:\nPresent feasible lunch options to user\n(e.g., 'Option A: 2 large pizzas from Pizza Place for $90.\nOption B: Assorted sushi from Sushi Spot for $95').", fillcolor="#ffd8a8"]; Task6 [label="Sub-Task 6:\nAwait user's choice and confirm order details.", fillcolor="#ffc9c9"]; Goal -> PlanGeneration; PlanGeneration -> Task1 [label="Step A"]; PlanGeneration -> Task2 [label="Step B"]; Task1 -> Task3 [label="Info for"]; Task2 -> Task3 [label="Info for"]; Task3 -> Task4; Task4 -> Task5; Task5 -> Task6; }The LLM reasons about the user's request and outlines a sequence of sub-tasks, potentially involving different tools or checks, to achieve the overall objective.Simple Planning: A Starting PointThe kind of planning we're discussing here is often quite straightforward. It might involve the LLM generating a numbered list of actions it intends to take, or making a decision between two or three possible next steps based on the current situation. This is different from complex, long-range planning algorithms found in traditional AI, but it's a significant step up from non-agentic LLM interactions.For instance, if an agent is asked, "What was the score of the last Giants game, and what’s their next scheduled game?" the LLM might internally decide:Use a sports API tool to find the score of the most recent Giants game.Use the same tool (or another) to find their next scheduled game.Combine this information into a coherent answer.This internal "decision tree" or sequence is a form of basic planning. It ensures the agent gathers all necessary information and performs actions in a logical order. As you progress in your understanding of LLM agents, you'll encounter more sophisticated planning techniques (like those discussed in Chapter 5, such as Chain-of-Thought or ReAct). For now, the important takeaway is that agents can, and often must, "think" about the sequence of their actions to effectively complete tasks. This planning capability is a building block for more autonomous and useful agent behavior.