Formulate multi-step strategies to achieve complex goals through autonomous task decomposition
Master the art of breaking down complex tasks into manageable steps with intelligent planning agents
Quick Overview
Complex problems often cannot be solved with a single action and require foresight to achieve a desired outcome. Without a structured approach, an agentic system struggles to handle multifaceted requests that involve multiple steps and dependencies.
The Planning pattern offers a standardized solution by having an agentic system first create a coherent plan to address a goal. It involves decomposing a high-level objective into a sequence of smaller, actionable steps or sub-goals.
Use this pattern when a user's request is too complex to be handled by a single action or tool. It is ideal for automating multi-step processes, such as generating a detailed research report or executing a competitive analysis.
For Beginners
Imagine you're planning a road trip from New York to Los Angeles. You don't just start driving randomly! You break it down: check the route, identify stops for gas and food, book hotels, estimate driving time for each day.
Planning agents work the same way. When given a complex task like "analyze this dataset and create a report," they don't jump straight to execution. They first create a step-by-step plan: load data → clean data → analyze patterns → generate visualizations → write summary → compile report.
Break down the goal into smaller, manageable sub-tasks
Create a sequence of actions with dependencies
Execute each step in order, adapting as needed
Check results and adjust plan if necessary
Planning pattern workflow diagram showing task analysis, plan generation, execution, and verification
Topic: planning
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Interleaves reasoning and action steps. The agent thinks about what to do, takes an action, observes the result, then reasons about the next step.
Thought → Action → Observation → Thought → ...Creates a complete plan upfront, then executes each step. More efficient but less adaptive to changes.
Plan All Steps → Execute Step 1 → Execute Step 2 → ...Breaks tasks into high-level goals, then decomposes each goal into sub-tasks recursively.
Goal → Sub-goals → Tasks → Sub-tasksPlanning is especially powerful for multi-step tasks where the order matters and where intermediate results inform future actions. It's the difference between "winging it" and having a roadmap.
A hallmark of planning is adaptability. An initial plan is merely a starting point, not a rigid script. The agent's real power is its ability to incorporate new information and steer the project around obstacles.
However, it is crucial to recognize the trade-off between flexibility and predictability. Dynamic planning is a specific tool, not a universal solution. When a problem's solution is already well-understood and repeatable, constraining the agent to a predetermined, fixed workflow is more effective.
The decision to use a planning agent versus a simple task-execution agent hinges on a single question: does the "how" need to be discovered, or is it already known?
Google Gemini DeepResearch is an agent-based system designed for autonomous information retrieval and synthesis. It functions through a multi-step agentic pipeline that dynamically and iteratively queries Google Search to systematically explore complex topics.
Deconstructs a user's prompt into a multi-point research plan, presented to the user for review and modification before execution.
Dynamically formulates and refines queries based on gathered information, actively identifying knowledge gaps, corroborating data points, and resolving discrepancies.
Manages the investigation asynchronously, analyzing hundreds of sources while being resilient to single-point failures.
Performs critical evaluation of collected information, identifying major themes and organizing content into a coherent narrative with logical sections, citations, and interactive features.
The OpenAI Deep Research API is a specialized tool designed to automate complex research tasks. It utilizes an advanced, agentic model (like o3-deep-research-2025-06-26) that can independently reason, plan, and synthesize information from real-world sources.
Produces well-organized reports with inline citations linked to source metadata, ensuring claims are verifiable and data-backed.
Exposes all intermediate steps, including the agent's reasoning, specific web search queries, and any code it ran.
Supports Model Context Protocol (MCP), enabling connection to private knowledge bases and internal data sources.
response = client.responses.create(
model="o3-deep-research-2025-06-26",
input=[
{"role": "developer", "content": [{"type": "input_text", "text": system_message}]},
{"role": "user", "content": [{"type": "input_text", "text": user_query}]}
],
reasoning={"summary": "auto"},
tools=[{"type": "web_search_preview"}]
)Break complex objectives into manageable sub-tasks
Execute steps in logical order with dependencies
Adjust plans based on intermediate results
Synthesize results into final outcome
1. Planning enables agents to break down complex goals into actionable, sequential steps
2. It is essential for handling multi-step tasks, workflow automation, and navigating complex environments
3. LLMs can perform planning by generating step-by-step approaches based on task descriptions
4. Explicitly prompting or designing tasks to require planning steps encourages this behavior in agent frameworks
5. Google Deep Research and OpenAI Deep Research exemplify advanced planning systems that reflect, plan, and execute autonomously