Planning in AI Agents

Overview

Planning is a foundational design pattern in Agentic AI, enabling agents to dynamically decide the sequence of steps needed to accomplish complex tasks. Instead of relying on predefined workflows, agents equipped with Planning capabilities can decompose a problem into smaller subtasks, determine the appropriate tools or actions for each subtask, and execute them autonomously. This adaptability is critical for handling tasks where the steps cannot be fully anticipated in advance.

How Planning Works

  1. Task Decomposition: The agent begins by analyzing the task and breaking it into smaller, manageable subtasks.

  2. Dynamic Decision-Making: At each step, the agent decides which tool or action is most appropriate based on the current context and the observations from previous steps.

  3. Execution and Observation: The agent performs the chosen action or invokes a tool, observes the output, and incorporates the results into its subsequent decisions.

  4. Iterative Process: This cycle of decision-making and execution continues until the agent determines the task is complete.

Planning agents often operate in a loop where they decide dynamically whether more actions or tool calls are needed. This iterative process ensures flexibility and allows the agent to adapt to unexpected changes in the environment.

Planning"

Plan-and-Execute Style vs. ReAct Style

Planning in agents often contrasts with the more typical ReAct-style approach, where an agent makes decisions one step at a time. In ReAct-style agents, the focus is on iterative decision-making, evaluating the need for the next step based on the current state.

However, a "plan-and-execute" style agent creates an explicit long-term plan at the outset, executing steps sequentially or as needed. This approach has several advantages:

  • Explicit Long-Term Planning: Planning agents map out a complete sequence of steps at the start, which is particularly useful for tasks that require maintaining coherence and direction over multiple steps. This compensates for limitations of even the most advanced LLMs, which can struggle with long-term reasoning.

  • Efficient Model Usage: Planning agents can delegate execution to smaller or less expensive models while reserving larger, more capable models for the planning phase. This makes the approach cost-efficient and scalable.

  • Traceability: Planning agents leave behind a clear trace or log of their steps, making it easier to debug and analyze their reasoning and execution paths.

Example Use Case: Plan-and-Execute Workflow

A "plan-and-execute" workflow involves breaking down a user request into distinct tasks, executing them sequentially, and dynamically adjusting the plan based on intermediate results. Below is a step-by-step explanation aligned with the diagram:

  1. User Request: The user provides a high-level goal or query, such as, "Summarize the research on renewable energy and create a report."

  2. Generate Tasks (Planning Phase): The agent analyzes the request and generates a list of tasks, such as:

    • Find key research articles.

    • Extract insights from the articles.

    • Write a summary.

  3. Task Execution (Single-Task Agent):

    • The Single-Task Agent picks tasks from the list and executes them one by one.

    • For instance, it may start by querying a database for relevant articles, then extract insights.

  4. Update State: After completing each task, the agent updates its state with the results, ensuring subsequent tasks have access to the latest data.

  5. Replan (if needed):

    • If the results of a task indicate new or modified requirements, the agent replans to adjust the remaining tasks.

    • For instance, if the article search yields too few results, the agent might decide to refine the search criteria.

  6. Respond to User: Once all tasks are completed, the agent compiles the results and delivers a comprehensive response to the user.

Benefits of Planning

  • Adaptability: Allows agents to respond dynamically to changes or errors in their environment.

  • Decomposition: Breaks down complex tasks into manageable steps, ensuring clarity and structure.

  • Cost Efficiency: Delegates execution to smaller models, using more powerful models only for planning.

  • Traceability: Maintains a clear record of decisions and actions, enabling better debugging and analysis.