6. Planning - Decomposing Big Problems into Solvable Steps
Planning is The Architect of Your AI. Teach agents to think before they act.
Planning — Decomposing Big Problems into Solvable Steps
Planning is an agentic pattern where the AI first breaks down a complex, multi-step goal into a sequence of smaller, actionable tasks, and then executes that plan to reach the final objective.
The Architect of Your AI. Teach agents to think before they act.
If Tool Use gives an agent hands, Planning gives it a strategic mind. This pattern is essential for tackling complex, ambiguous goals that cannot be solved by a single tool or a predefined chain. Instead of reacting step-by-step, the agent first formulates a complete strategy. For a business, this enables the creation of autonomous agents that can handle high-level requests like "research competitors and generate a report" or "plan a marketing campaign for our new product," tasks that require foresight and multi-step execution.
📊 Video and Diagram
A visual of the Planning flow:
High-Level Goal -> [Planner LLM: Create Step-by-Step Plan] -> [Executor Agent: Executes Step 1 -> Executes Step 2 -> Executes Step 3...] -> Final Result
Build a Plan-and-Execute Agent
YouTube: Build a "Plan and Execute" AI Agent Workflow with LangGraph
This video provides a clear, code-driven explanation of how planner and executor agents collaborate, letting you see real-world plan-and-execute architecture in action.
The ReAct Framework
YouTube: ReAct: Synergizing Reasoning and Acting in Language Models
An accessible and practical explanation of the ReAct paper, including how LLM agents interleave planning (“thought”) and real-world actions, for more dynamic and robust workflows.
🚩 What Is Planning?
"Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat." - Sun Tzu
The Planning pattern involves two main components: a Planner and an Executor. The Planner, typically a powerful LLM, receives a high-level goal and generates a list of discrete steps to achieve it. The Executor then takes this list and carries out each task one by one, often using other patterns like Tool Use for each step. The intended outcome is a robust and transparent workflow for solving complex problems.
🏗 Use Cases
Scenario: A business analyst needs to create a comprehensive report on the market viability of a new product idea: a smart coffee mug.
Applying the Pattern:
Goal Definition: The analyst gives the agent the high-level goal: "Generate a market viability report for a new smart coffee mug."
Planning Step: The Planner LLM breaks this down into a concrete, multi-step plan.
Execution Step: The Executor agent begins carrying out the plan, using a web search tool for the research tasks and its internal language capabilities for the writing and synthesis tasks.
Outcome: The agent autonomously produces a well-structured, detailed report by following a logical, pre-defined strategy, a task that would have been far too complex for a single prompt.
General Use: This pattern is ideal for any goal that is ambiguous or requires multiple distinct steps to complete.
Complex Research Queries: "Write a detailed history of the Roman Empire, including key figures, major battles, and cultural impact."
Autonomous Task Management: "Organize my upcoming trip to Tokyo by finding flights, booking a hotel near Shibuya, and creating a 3-day itinerary."
Creative Projects: "Write a short sci-fi story about a robot who discovers music. Include character backstories and a plot outline."
💻 Sample Code / Pseudocode
This Python pseudocode illustrates the core logic of a Planner and an Executor working together.
In Python
# --- Tool Definition ---
def web_search(query: str):
"""A dummy tool to simulate searching the web."""
print(f"--- TOOL: Searching web for '{query}' ---")
return f"Found several articles about '{query}'."
# --- Agent Logic ---
class PlanningAgent:
def create_plan(self, goal: str) -> list[str]:
"""Simulates a Planner LLM creating a list of steps."""
print(f"--- PLANNER: Creating plan for goal: '{goal}' ---")
# In a real system, this would be a sophisticated LLM call.
plan = [
"Search for the main topic of the goal.",
"Find three key facts about the topic.",
"Write a summary paragraph incorporating the key facts."
]
return plan
def execute_step(self, step: str):
"""Simulates an Executor agent carrying out a single step."""
print(f"\n--- EXECUTOR: Executing step: '{step}' ---")
# The executor would often use other tools (like routing) here.
if "Search for" in step:
query = step.replace("Search for", "").strip()
return web_search(query)
elif "Find three key facts" in step:
return "Fact 1, Fact 2, Fact 3."
elif "Write a summary" in step:
return "This is the final summary based on the facts found."
return "Step execution failed."
def run(self, goal: str):
"""Runs the full plan-and-execute workflow."""
plan = self.create_plan(goal)
print(f"\n--- PLANNER: Generated Plan: {plan} ---")
results = []
for step in plan:
result = self.execute_step(step)
results.append(result)
print("\n--- SYNTHESIZER: Combining all results... ---")
final_report = "\n".join(results)
return final_report
# --- Execute the workflow ---
agent = PlanningAgent()
final_result = agent.run("Write a report on renewable energy.")
print("\n--- FINAL REPORT ---")
print(final_result)
🟢 Pros
Handles Complexity: The most effective pattern for ambiguous, high-level, multi-step goals.
Robustness & Recoverability: If a single step fails, the agent can potentially retry it or even re-plan without starting the entire process from scratch.
Transparency: The generated plan provides a clear, auditable trail of the agent's "thought process," making it easier to debug and understand its actions.
🔴 Cons
Increased Latency: The initial planning step adds a significant delay before any action is taken. The process is not immediate.
Plan Rigidity: A simple executor might follow a flawed plan to the end without adapting. More advanced agents require complex "re-planning" logic if a step's result is unexpected.
Cost: Often requires multiple LLM calls: one for the initial plan and potentially more for each execution step, making it more expensive.
🛑 Anti-Patterns (Mistakes to Avoid)
Overly Detailed Planning: Don't prompt the planner to create an extremely granular plan. This can be brittle. High-level steps are more robust.
No Failure Handling: The executor must be designed to handle a step that fails. Simply crashing or stopping is not a viable strategy.
Ignoring Step Results: A basic executor that just runs through the plan without considering the output of each step is not truly intelligent. The results of one step should inform the next.
🛠 Best Practices
Keep Plans High-Level: The planner should define the "what," not the "how." Let the executor (with its tools) figure out the details of each step.
Include Validation Steps: A good planner will include steps in its plan like "Review the gathered data for inconsistencies" or "Verify the code runs without errors."
Dynamic Re-planning: For advanced agents, implement a reflection step where the agent reviews the plan's progress after each step and can modify the remaining plan if necessary.
🧪 Sample Test Plan
Unit Tests (Planner): Test the planner's ability to generate logical, coherent, and relevant plans for a variety of goals. Assert that the plan contains expected keywords or steps.
Unit Tests (Executor): Test each tool the executor can use independently.
End-to-End Tests: Provide a high-level goal and run the entire agent. Evaluate the final output for quality and accuracy. This is the most important test.
Performance Tests: Measure the latency of the planning step and each execution step to identify bottlenecks.
🤖 LLM as Judge/Evaluator
Recommendation: Use a powerful judge LLM to evaluate the quality and logical coherence of the plan itself.
How to Apply: Show the judge LLM the initial goal and the generated plan. Ask it: "On a scale of 1-10, how likely is this plan to successfully achieve the goal? Identify any missing steps or logical flaws." This helps you iterate on the planner's prompt.
🗂 Cheatsheet
Variant: Plan-and-Solve
When to Use: For problems that require a static plan created entirely up-front.
Key Tip: Use your most powerful LLM for the planning stage, as the quality of the entire outcome depends on it.
Variant: ReAct (Reason+Act)
When to Use: For dynamic problems where the world can change, requiring the agent to adapt.
Key Tip: The agent interleaves
Thought(a brief reasoning/planning step) andAction(executing one tool). This is more of a continuous, step-by-step planning process.
Relevant Content
ReAct Paper (arXiv:2210.03629): https://arxiv.org/abs/2210.03629 The foundational paper from Google Research that introduced the concept of interleaving reasoning and acting, a cornerstone of modern agent design.
Plan-and-Solve Paper (arXiv:2305.04091): https://arxiv.org/abs/2305.04091 This paper proposes a more deliberate approach where a planner first devises a complete plan which an executor then follows, improving performance on complex tasks.
LangChain Plan-and-Execute Agent: https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute The official documentation and implementation of this pattern within the LangChain framework.
📅 Coming Soon
Stay tuned for our next article in the series: Design Pattern: Multi-Agent Collaboration — Building Teams of AI Agents That Work Together.