Planning Pattern

Table of Contents

1. Introduction: From Reacting to Strategizing
2. The Core Concept: What is Planning?
   • An Analogy: The Expert Event Planner
3. The Crucial Trade-Off: Flexibility vs. Predictability
4. Practical Applications & Use Cases
5. Hands-On Example 1: CrewAI (Plan and Execute)
   • The Goal
   • The CrewAI Philosophy
   • The Code, Explained Step-by-Step
6. Advanced Planning in Action: Google & OpenAI Deep Research
   • The Concept: Autonomous Research Systems
   • The Process: A Dynamic, Iterative Loop
   • Key Benefits of Advanced Planning Systems
7. Final Summary & Key Takeaways
   • At a Glance
   • Key Takeaways Checklist

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1. Introduction: From Reacting to Strategizing

Intelligent behavior is more than just responding to the latest input. It requires foresight—the ability to look ahead, set a goal, and chart a course to get there. The previous patterns gave our agents powerful capabilities, but they were largely reactive. Planning elevates an agent from a simple doer to a strategic thinker.

At its core, the Planning pattern is the ability of an agent to autonomously formulate a sequence of actions to move from an initial state to a desired goal state. It’s about figuring out the “how” when it isn’t already known.

Why it matters: Planning is the bridge between a high-level human goal and the concrete, low-level steps an agent must execute. It is the foundational component for building autonomous systems that can handle complex, multi-step tasks in dynamic environments.

2. The Core Concept: What is Planning?

In the context of AI, a planning agent is a specialist you delegate a complex objective to. You define the what (the goal and its constraints), and the agent’s core task is to discover the how (the sequence of steps).

A key hallmark of this process is adaptability. An initial plan is just a starting point, not a rigid script. The agent’s real power lies in its ability to incorporate new information and navigate around obstacles. If a preferred hotel is booked or a data source is unavailable, a planning agent doesn’t fail; it adapts. It registers the new constraint, re-evaluates its options, and formulates a new, updated plan.

An Analogy: The Expert Event Planner

• A Non-Planning Agent: You give this agent a fixed to-do list: “1. Call Hotel A. 2. Book Caterer B. 3. Send invites.” If Hotel A is full, the agent stops and reports failure. It cannot strategize an alternative.
• A Planning Agent: You give this agent a goal: “Organize a team offsite for 20 people on a budget of $5,000 for next month.” The agent now has to create the plan. It will:
  1. Research venues that fit the budget and capacity.
  2. Check availability for those venues.
  3. Find caterers in the area.
  4. If the top venue is booked, it won’t fail. It will adapt its plan by moving to the next best option or suggesting alternative dates. It continually re-plans to steer around obstacles and reach the goal.

3. The Crucial Trade-Off: Flexibility vs. Predictability

Dynamic planning is a powerful tool, but it is not a universal solution. The decision to use a planning agent hinges on a single question: Does the “how” need to be discovered, or is it already known?

• Use a Planning Agent When: The problem is complex, dynamic, and the path to the solution is not known in advance. The agent needs autonomy and flexibility to succeed.
  • Example: “Research the impact of AI on the European job market and write a report.”
• Use a Fixed Workflow (e.g., Prompt Chaining) When: The problem’s solution is well-understood, repeatable, and requires a predictable, consistent outcome. Limiting the agent’s autonomy reduces uncertainty and risk.
  • Example: “Extract the name, date, and invoice total from this document and save it as JSON.”

4. Practical Applications & Use Cases

Planning is a core process for any system that needs to perform a coherent sequence of interdependent actions.

• Procedural Task Automation:
  • Use Case: Onboarding a new employee.
  • Plan: The agent creates a sequence of steps: 1. Create system accounts. 2. Assign training modules. 3. Coordinate with HR and the hiring manager. 4. Schedule orientation meetings.
• Robotics and Autonomous Navigation:
  • Use Case: A delivery drone navigating a city.
  • Plan: The agent generates an optimal flight path from its current location to the destination, considering battery life, obstacles, and air traffic regulations.
• Structured Information Synthesis:
  • Use Case: Generating a comprehensive research report.
  • Plan: The agent devises distinct phases for its work: 1. Initial information gathering. 2. Data summarization. 3. Content structuring and outlining. 4. Iterative refinement and citation generation.
• Multi-Step Customer Support:
  • Use Case: Resolving a customer’s technical issue.
  • Plan: The agent creates a systematic plan for diagnosis (asking clarifying questions), solution implementation (guiding the user through steps), and, if necessary, escalation to a human agent.

5. Hands-On Example 1: CrewAI (Plan and Execute)

This example shows how an agent can be explicitly prompted to first create a plan and then execute that plan to complete a task.

The Goal

Build an agent that, when asked to write a summary on “The importance of Reinforcement Learning in AI,” will:

1. First, create a bullet-point plan for the summary.
2. Second, write a ~200-word summary based on that plan.
3. Present the plan and the summary as two distinct sections in the final output.

The CrewAI Philosophy

CrewAI allows you to structure an agent’s work through its Task definition. By clearly describing a multi-step process in the task’s description and expected_output, you guide the agent to first think (plan) and then act (execute).

The Code, Explained Step-by-Step

# --- 1. Define the Agent ---
# We create a single agent with a clear role: to be both a planner and a writer.
planner_writer_agent = Agent(
    role='Article Planner and Writer',
    goal='Plan and then write a concise, engaging summary on a specified topic.',
    backstory=(
        'You are an expert technical writer and content strategist. '
        'Your strength lies in creating a clear, actionable plan before writing...'
    ),
    llm=ChatOpenAI(model="gpt-4-turbo") # Assigning a specific, powerful LLM
)

# --- 2. Define the Task ---
# The task description explicitly tells the agent to perform two steps in order.
# The expected_output provides a rigid structure for the final result.
topic = "The importance of Reinforcement Learning in AI"
high_level_task = Task(
    description=(
        f"1. Create a bullet-point plan for a summary on the topic: '{topic}'.\n"
        f"2. Write the summary based on your plan, keeping it around 200 words."
    ),
    expected_output=(
        "A final report containing two distinct sections:\n\n"
        "### Plan\n"
        "- A bulleted list outlining the main points of the summary.\n\n"
        "### Summary\n"
        "- A concise and well-structured summary of the topic."
    ),
    agent=planner_writer_agent
)

# --- 3. Assemble and Run the Crew ---
# We set `process=Process.sequential` to ensure the tasks (in this case, one high-level task)
# are executed in a clear order.
crew = Crew(
    agents=[planner_writer_agent],
    tasks=[high_level_task],
    process=Process.sequential
)

# The kickoff() method starts the agent's planning and execution process.
result = crew.kickoff()
print(result)

6. Advanced Planning in Action: Google & OpenAI Deep Research

Systems like Google’s Deep Research and the OpenAI Deep Research API are premier examples of the Planning pattern in its most advanced form. They automate complex research tasks by creating and executing dynamic, multi-step plans.

The Concept: Autonomous Research Systems

These are not simple Q&A models. When given a high-level query (e.g., “What is the volume of VC investment in Europe?”), they don’t just give a single answer. They autonomously:

1. Reason about the problem.
2. Plan a series of research sub-questions.
3. Synthesize information from multiple real-world sources using built-in tools (like web search).
4. Deliver a structured, citation-rich final report.

The Process: A Dynamic, Iterative Loop

1. Deconstruction & Initial Plan: The system first breaks down the user’s prompt into a multi-point research plan.
2. Collaborative Refinement: This initial plan is often presented to the user for review and modification, allowing for a collaborative start.
3. Iterative Search & Analysis: Once the plan is approved, the agent begins an iterative loop. It’s not just executing a fixed list of searches; it dynamically formulates and refines its search queries based on the information it gathers, actively identifying knowledge gaps and resolving discrepancies along the way.
4. Synthesis and Citation: Finally, the system critically evaluates all the collected information, organizes it into a coherent narrative with logical sections, and provides citations that link back to the original sources for transparency.

Key Benefits of Advanced Planning Systems

• Transparency: Unlike a black-box answer, these systems can expose their intermediate steps—the reasoning, the specific web queries, the code run—allowing for deeper understanding and debugging.
• Structured Output: They produce well-organized, data-driven reports with verifiable, inline citations.
• Extensibility: They can often be connected to private knowledge bases, blending public web research with proprietary data.

7. Final Summary & Key Takeaways

At a Glance

What: Complex goals often require foresight and a sequence of actions. The Planning pattern provides a structured approach where an agent first decomposes a high-level objective into a manageable series of smaller, executable steps.

Why: Without planning, an agent is merely reactive and struggles with multifaceted requests. Planning transforms the agent into a strategic executor that can proactively work towards a complex objective, manage dependencies, and even adapt its plan when it encounters obstacles.

Rule of Thumb: Use the Planning pattern when a user’s request is too complex to be handled by a single action or tool. It is ideal for automating any multi-step process that requires a sequence of interdependent operations, such as generating a research report, onboarding an employee, or analyzing a complex dataset.

Key Takeaways Checklist

• Planning enables agents to break down complex goals into actionable, sequential steps.
• It is the key to handling multi-step tasks, workflow automation, and navigating complex and dynamic environments.
• LLMs excel at planning by generating plausible and effective step-by-step approaches based on a task description.
• A plan should be adaptable, allowing the agent to re-evaluate and change course when it encounters new information or obstacles.
• Advanced systems like Google Deep Research show the power of dynamic, iterative planning for complex, real-world information synthesis.