Thinking Before Acting

For an LLM agent to accomplish more than just a single, direct response, it needs a way to figure out a sequence of actions. This is where the idea of "thinking before acting," or basic planning, comes into play. Instead of simply reacting to the immediate input, an agent with planning capabilities can formulate a sequence of steps to achieve a more complex objective.

The LLM as the "Thinker"

At the heart 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:

1. What needs to be done first?
2. What information is required for the next step?
3. Which tool, if any, is appropriate for a particular step?
4. 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 Steps

Many 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.

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 Point

The 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:

1. Use a sports API tool to find the score of the most recent Giants game.
2. Use the same tool (or another) to find their next scheduled game.
3. 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.