Planning

Planning (sometimes called "Reasoning & Acting") decouples the strategy from the execution. Instead of reacting immediately to a user request, the agent pauses to decompose the goal into sub-goals, identifies dependencies, and creates an ordered list of steps. This allows agents to tackle complex, multi-step problems that require foresight.

When to Use

• Multi-Step Tasks: "Research X, then Y, then write a report comparing them."

• Dependency Management: When step B cannot start until step A is finished (e.g., compile code -> run tests).

• Resource Constraints: To optimize the usage of expensive tools or API calls.

• Error Recovery: If a step fails, the plan can be adjusted dynamically without restarting from scratch.

Use Cases

• Travel Itinerary: Searching for flights, hotels, and activities, then checking availability, then booking.

• Software Development: Designing a system -> Writing code -> Writing documentation.

• Data Analysis: Plan -> Data Collection -> Cleaning -> Analysis -> Visualization.

Implementation Pattern

def planning_workflow(goal): # Step 1: Create Plan # The planner generates a list of steps, not the actual work. plan = planner_agent.run( prompt="Create a step-by-step plan to achieve this goal...", input=goal )

results = {}

# Step 2: Execute Plan
for step in plan.steps:
    # Check dependencies
    if not check_dependencies(step, results):
        raise DepedencyError(f"Cannot execute {step.id}")
        
    # Execute the specific step using a worker agent
    result = worker_agent.run(
        prompt=f"Execute this step: {step.description}",
        context=results # Pass context from previous steps
    )
    
    results[step.id] = result
    
# Step 3: Summarize
return synthesizer_agent.run(results)