Chapter 6: Planning

第 6 章：规划

Intelligent behavior often involves more than just reacting to the immediate input. It requires foresight, breaking down complex tasks into smaller, manageable steps, and strategizing how to achieve a desired outcome. This is where the Planning pattern comes into play. At its core, planning is the ability for an agent or a system of agents to formulate a sequence of actions to move from an initial state towards a goal state.

智能行为通常不仅需要对即时输入做出反应，还需要远见卓识、将复杂任务分解为更小的可管理步骤，以及制定实现预期结果的策略。这就是规划模式发挥作用的地方。从本质上讲，规划是指一个智能体或智能体系统构思一系列行动，从初始状态向目标状态推进的能力。

Planning Pattern Overview

规划模式概述

In the context of AI, it’s helpful to think of a planning agent as a specialist to whom you delegate a complex goal. When you ask it to “organize a team offsite,” you are defining the what—the objective and its constraints—but not the how. The agent’s core task is to autonomously chart a course to that goal. It must first understand the initial state (e.g., budget, number of participants, desired dates) and the goal state (a successfully booked offsite), and then discover the optimal sequence of actions to connect them. The plan is not known in advance; it is created in response to the request.

在 AI 背景下，可将规划智能体视为处理复杂目标的专家。当您要求”组织团队外出活动”时，您只需定义”目标”（内容及约束），而无需指定”方法”。智能体的核心任务是自主规划实现目标的路径：先理解初始状态（如预算、参与人数、期望日期）和目标状态（成功预订活动），再设计连接两者的最优行动序列。计划并非预先设定，而是根据请求动态生成。

A hallmark of this process 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. For instance, if the preferred venue becomes unavailable or a chosen caterer is fully booked, a capable agent doesn’t simply fail. It adapts. It registers the new constraint, re-evaluates its options, and formulates a new plan, perhaps by suggesting alternative venues or dates.

此过程的核心在于适应性。初始计划仅是起点而非固定脚本，智能体的真正价值在于整合新信息、规避障碍的能力。例如，当首选场地不可用或餐饮服务商满员时，高效智能体不会失败，而是记录新约束、重新评估选项，并制定新计划（如推荐替代场地或日期）。

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. This approach limits the agent’s autonomy to reduce uncertainty and the risk of unpredictable behavior, guaranteeing a reliable and consistent outcome. Therefore, 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?

然而，认识到灵活性与可预测性之间的权衡至关重要。动态规划是一种特定工具，而非通用解决方案。当问题的解决方案已被充分理解且可重复时，将智能体限制在预先确定的固定工作流中会更有效。这种方法通过限制智能体的自主权来减少不确定性和不可预测行为的风险，确保结果可靠且一致。因此，是使用规划智能体还是使用预定义工作流的决定取决于一个关键问题：「方法」需要被发现，还是已经已知？

Practical Applications & Use Cases

实际应用与用例

The Planning pattern is a core computational process in autonomous systems, enabling an agent to synthesize a sequence of actions to achieve a specified goal, particularly within dynamic or complex environments. This process transforms a high-level objective into a structured plan composed of discrete, executable steps.

规划模式是自主系统中的核心计算过程，使智能体综合一系列行动以实现指定目标，特别是在动态或复杂环境中。这个过程将高级目标转换为由离散可执行步骤组成的结构化计划。

In domains such as procedural task automation, planning is used to orchestrate complex workflows. For example, a business process like onboarding a new employee can be decomposed into a directed sequence of sub-tasks, such as creating system accounts, assigning training modules, and coordinating with different departments. The agent generates a plan to execute these steps in a logical order, invoking necessary tools or interacting with various systems to manage dependencies.

在过程任务自动化等领域，规划用于编排复杂的工作流。例如，像新员工入职这样的业务流程可以分解为定向的子任务序列，例如创建系统帐户、分配培训模块和与不同部门协调。智能体生成一个计划以逻辑顺序执行这些步骤，调用必要的工具或与各种系统交互以管理依赖关系。

Within robotics and autonomous navigation, planning is fundamental for state-space traversal. A system, whether a physical robot or a virtual entity, must generate a path or sequence of actions to transition from an initial state to a goal state. This involves optimizing for metrics such as time or energy consumption while adhering to environmental constraints, like avoiding obstacles or following traffic regulations.

在机器人和自主导航中，规划对于状态空间遍历是基础性的。一个系统，无论是物理机器人还是虚拟实体，都必须生成路径或行动序列以从初始状态转换到目标状态。这涉及优化时间或能源消耗等指标，同时遵守环境约束，如避开障碍物或遵守交通规则。

This pattern is also critical for structured information synthesis. When tasked with generating a complex output like a research report, an agent can formulate a plan that includes distinct phases for information gathering, data summarization, content structuring, and iterative refinement. Similarly, in customer support scenarios involving multi-step problem resolution, an agent can create and follow a systematic plan for diagnosis, solution implementation, and escalation.

此模式对于结构化信息综合也至关重要。当被要求生成像研究报告这样的复杂输出时，智能体可以制定一个包括信息收集、数据总结、内容结构化和迭代完善的不同阶段的计划。同样，在涉及多步问题解决的客户支持场景中，智能体可以创建并遵循诊断、解决方案实施和升级的系统计划。

In essence, the Planning pattern allows an agent to move beyond simple, reactive actions to goal-oriented behavior. It provides the logical framework necessary to solve problems that require a coherent sequence of interdependent operations.

从本质上讲，规划模式允许智能体从简单的反应性行动转向目标导向的行为。它提供了解决需要一系列相互依赖操作的问题所必需的逻辑框架。

Hands-on code (Crew AI)

实操代码（Crew AI）

The following section will demonstrate an implementation of the Planner pattern using the Crew AI framework. This pattern involves an agent that first formulates a multi-step plan to address a complex query and then executes that plan sequentially.

以下部分将演示使用 Crew AI 框架实现规划模式。此模式涉及一个智能体，它首先制定多步计划以解决复杂查询，然后顺序执行该计划。

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import os
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI

## Load environment variables from .env file for security
load_dotenv()

## 1. Explicitly define the language model for clarity
llm = ChatOpenAI(model="gpt-4-turbo")

## 2. Define a clear and focused agent
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, '
       'ensuring the final summary is both informative and easy to digest.'
   ),
   verbose=True,
   allow_delegation=False,
   llm=llm # Assign the specific LLM to the agent
)

## 3. Define a task with a more structured and specific expected output
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,
)

## Create the crew with a clear process
crew = Crew(
   agents=[planner_writer_agent],
   tasks=[high_level_task],
   process=Process.sequential,
)

## Execute the task
print("## Running the planning and writing task ##")
result = crew.kickoff()
print("\n\n---\n## Task Result ##\n---")
print(result)

This code uses the CrewAI library to create an AI agent that plans and writes a summary on a given topic. It starts by importing necessary libraries, including Crew.ai and langchain_openai, and loading environment variables from a .env file. A ChatOpenAI language model is explicitly defined for use with the agent. An Agent named planner_writer_agent is created with a specific role and goal: to plan and then write a concise summary. The agent’s backstory emphasizes its expertise in planning and technical writing. A Task is defined with a clear description to first create a plan and then write a summary on the topic “The importance of Reinforcement Learning in AI”, with a specific format for the expected output. A Crew is assembled with the agent and task, set to process them sequentially. Finally, the crew.kickoff() method is called to execute the defined task and the result is printed.

此代码使用 CrewAI 库创建一个 AI 智能体，该智能体规划并撰写关于给定主题的摘要。它首先导入必要的库，包括 CrewAI 和 langchain_openai，并从 .env 文件加载环境变量。明确定义了一个 ChatOpenAI 语言模型供智能体使用，创建了一个名为 planner_writer_agent 的智能体，具有特定的角色和目标：规划然后撰写简洁的摘要。智能体的背景故事强调其在规划和技术写作方面的专业知识。定义了一个任务，明确描述首先创建计划，然后撰写关于”强化学习在 AI 中的重要性”主题的摘要，并为预期输出指定了特定格式。组建了一个包含智能体和任务的团队，设置为顺序处理它们。最后，调用 crew.kickoff() 方法执行定义的任务并打印结果。

Google DeepResearch

Google DeepResearch

Google Gemini DeepResearch (see Fig.1) 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. The system is engineered to process a large corpus of web-based sources, evaluate the collected data for relevance and knowledge gaps, and perform subsequent searches to address them. The final output consolidates the vetted information into a structured, multi-page summary with citations to the original sources.

Google Gemini DeepResearch（见图 1）是一个基于智能体的自主信息检索与综合系统。它通过多步智能体流水线动态、迭代地查询 Google 搜索，以系统性地探索复杂主题。该系统旨在处理大量网络资源，评估所收集数据的相关性和知识缺口，并执行后续搜索来填补这些缺口。最终输出将经过验证的信息整合为结构化的多页摘要，并引用原始来源。

Expanding on this, the system’s operation is not a single query-response event but a managed, long-running process. It begins by deconstructing a user’s prompt into a multi-point research plan (see Fig. 1), which is then presented to the user for review and modification. This allows for a collaborative shaping of the research trajectory before execution. Once the plan is approved, the agentic pipeline initiates its iterative search-and-analysis loop. This involves more than just executing a series of predefined searches; the agent dynamically formulates and refines its queries based on the information it gathers, actively identifying knowledge gaps, corroborating data points, and resolving discrepancies.

进一步来说，该系统的运作不是单一的查询-响应事件，而是一个受管理的长时运行流程。它首先将用户提示分解为多点研究计划（见图 1），然后呈现给用户审阅和修改。这使得在执行前可以协作塑造研究轨迹。计划获得批准后，智能体流水线启动迭代搜索-分析循环。这不仅限于执行一系列预定义搜索；智能体会根据收集的信息动态制定和优化查询，主动识别知识缺口、验证数据点并解决差异。

Fig. 1: Google Deep Research agent generating an execution plan for using Google Search as a tool.

图 1：Google Deep Research智能体使用 Google 搜索作为工具的执行计划。

A key architectural component is the system’s ability to manage this process asynchronously. This design ensures that the investigation, which can involve analyzing hundreds of sources, is resilient to single-point failures and allows the user to disengage and be notified upon completion. The system can also integrate user-provided documents, combining information from private sources with its web-based research. The final output is not merely a concatenated list of findings but a structured, multi-page report. During the synthesis phase, the model performs a critical evaluation of the collected information, identifying major themes and organizing the content into a coherent narrative with logical sections. The report is designed to be interactive, often including features like an audio overview, charts, and links to the original cited sources, allowing for verification and further exploration by the user. In addition to the synthesized results, the model explicitly returns the full list of sources it searched and consulted (see Fig.2). These are presented as citations, providing complete transparency and direct access to the primary information. This entire process transforms a simple query into a comprehensive, synthesized body of knowledge.

关键的架构组件是系统异步管理此流程的能力。这种设计确保了可能涉及分析数百个来源的调查能够抵御单点故障，并允许用户脱离并在完成时收到通知。系统还可以整合用户提供的文档，将私有来源的信息与其基于网络的研究相结合。最终输出不仅仅是发现的串联列表，而是一份结构化的多页报告。在综合阶段，模型对收集的信息进行批判性评估，识别主要主题，并将内容组织成具有逻辑章节的连贯叙述。报告设计为交互式的，通常包含音频概述、图表和指向原始引用来源的链接等功能，允许用户验证和进一步探索。除了综合结果外，模型还明确返回它搜索和咨询的完整来源列表（见图 2）。这些以引用形式呈现，提供完全的透明度和对主要信息的直接访问。整个过程将简单查询转化为全面、综合的知识体系。

Fig. 2: An example of Deep Research plan being executed, resulting in Google Search being used as a tool to search various web sources.

图 2：Deep Research 计划执行的示例，导致使用 Google 搜索作为工具搜索各种网络来源。

By mitigating the substantial time and resource investment required for manual data acquisition and synthesis, Gemini DeepResearch provides a more structured and exhaustive method for information discovery. The system’s value is particularly evident in complex, multi-faceted research tasks across various domains.

通过减轻手动数据获取和综合所需的大量时间和资源投资，Gemini DeepResearch 为信息发现提供了更结构化和详尽的方法。该系统的价值在跨各个领域的复杂、多方面研究任务中尤为明显。

For instance, in competitive analysis, the agent can be directed to systematically gather and collate data on market trends, competitor product specifications, public sentiment from diverse online sources, and marketing strategies. This automated process replaces the laborious task of manually tracking multiple competitors, allowing analysts to focus on higher-order strategic interpretation rather than data collection (see Fig. 3).

例如，在竞争分析中，可以指示智能体自动地收集和整理关于市场趋势、竞争对手产品规格、来自各种在线来源的公众情绪和营销策略的数据。这个自动化过程取代了手动跟踪多个竞争对手的繁重任务，使分析师能够专注于更高层次的战略解释而不是数据收集（见图 3）。

Fig. 3: Final output generated by the Google Deep Research agent, analyzing on our behalf sources obtained using Google Search as a tool.

图 3：Google Deep Research智能体的最终输出，代表我们分析使用 Google 搜索作为工具获得的来源。

Similarly, in academic exploration, the system serves as a powerful tool for conducting extensive literature reviews. It can identify and summarize foundational papers, trace the development of concepts across numerous publications, and map out emerging research fronts within a specific field, thereby accelerating the initial and most time-consuming phase of academic inquiry.

同样，在学术探索中，该系统作为进行广泛文献综述的强大工具。它可以识别和总结基础论文，追踪概念在众多出版物中的发展，并绘制特定领域内新兴研究前沿的地图，从而加速学术探究的初始和最耗时的阶段。

The efficiency of this approach stems from the automation of the iterative search-and-filter cycle, which is a core bottleneck in manual research. Comprehensiveness is achieved by the system’s capacity to process a larger volume and variety of information sources than is typically feasible for a human researcher within a comparable timeframe. This broader scope of analysis helps to reduce the potential for selection bias and increases the likelihood of uncovering less obvious but potentially critical information, leading to a more robust and well-supported understanding of the subject matter.

这种方法的效率源于迭代搜索和过滤周期的自动化，这是手动研究的核心瓶颈。通过系统处理比人类研究人员在可比时间框架内通常可行的更大量和更多样化的信息来源来实现全面性。这种更广泛的分析范围有助于减少选择偏差的潜力，并增加发现不太明显但可能至关重要的信息的可能性，从而导致对主题更稳健和充分支持的理解。

OpenAI Deep Research API

OpenAI Deep Research API

The OpenAI Deep Research API is a specialized tool designed to automate complex research tasks. It utilizes an advanced, agentic model that can independently reason, plan, and synthesize information from real-world sources. Unlike a simple Q&A model, it takes a high-level query and autonomously breaks it down into sub-questions, performs web searches using its built-in tools, and delivers a structured, citation-rich final report. The API provides direct programmatic access to this entire process, using at the time of writing models like o3-deep-research-2025-06-26 for high-quality synthesis and the faster o4-mini-deep-research-2025-06-26 for latency-sensitive application

OpenAI DeepResearch API 是专为自动化复杂研究任务设计的工具。它采用先进智能体，能够独立推理、规划并综合现实世界信息。不同于简单问答模型，它接受高级查询后自主分解为子问题，利用内置工具执行网络搜索，最终生成带引用的结构化报告。该 API 提供全流程程序化访问，支持高质量综合模型（如 o3-deep-research-2025-06-26）和低延迟模型（如 o4-mini-deep-research-2025-06-26）。

The Deep Research API is useful because it automates what would otherwise be hours of manual research, delivering professional-grade, data-driven reports suitable for informing business strategy, investment decisions, or policy recommendations. Its key benefits include:

Deep Research API 很有用，因为它自动化了原本需要数小时的手动研究，提供适合为业务战略、投资决策或政策建议提供信息的专业级、数据驱动的报告。其主要好处包括：

• Structured, Cited Output: It produces well-organized reports with inline citations linked to source metadata, ensuring claims are verifiable and data-backed.

• 结构化、引用的输出： 它产生组织良好的报告，带有链接到来源元数据的内联引用，确保声明可验证且有数据支持。

• Transparency: Unlike the abstracted process in ChatGPT, the API exposes all intermediate steps, including the agent’s reasoning, the specific web search queries it executed, and any code it ran. This allows for detailed debugging, analysis, and a deeper understanding of how the final answer was constructed.

• 透明度： 与 ChatGPT 中的抽象过程不同，API 公开所有中间步骤，包括智能体的推理、它执行的特定网络搜索查询以及它运行的任何代码。这允许详细的调试、分析以及更深入地理解最终答案是如何构建的。

• Extensibility: It supports the Model Context Protocol (MCP), enabling developers to connect the agent to private knowledge bases and internal data sources, blending public web research with proprietary information.
• 可扩展性： 它支持模型上下文协议（MCP），使开发人员能够将智能体连接到私有知识库和内部数据源，将公共网络研究与专有信息混合。

To use the API, you send a request to the client.responses.create endpoint, specifying a model, an input prompt, and the tools the agent can use. The input typically includes a system_message that defines the agent’s persona and desired output format, along with the user_query. You must also include the web_search_preview tool and can optionally add others like code_interpreter or custom MCP tools (see Chapter 10) for internal data.

要使用 API，您向 client.responses.create 端点发送请求，指定模型、输入提示词和智能体使用的工具。输入通常包括定义智能体角色和期望输出格式的 system_message，以及 user_query。您还必须包括 web_search_preview 工具，并可以选择添加其他工具，如 code_interpreter 或自定义 MCP 工具（见第 10 章）用于内部数据。

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from openai import OpenAI

## Initialize the client with your API key
client = OpenAI(api_key="YOUR_OPENAI_API_KEY")

## Define the agent's role and the user's research question
system_message = """You are a professional researcher preparing a structured, data-driven report. Focus on data-rich insights, use reliable sources, and include inline citations."""
user_query = "Research the economic impact of semaglutide on global healthcare systems."

## Create the Deep Research API call
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"}]
)

## Access and print the final report from the response
final_report = response.output[-1].content[0].text
print(final_report)

## --- ACCESS INLINE CITATIONS AND METADATA ---
print("--- CITATIONS ---")
annotations = response.output[-1].content[0].annotations
if not annotations:
   print("No annotations found in the report.")
else:
   for i, citation in enumerate(annotations):
       # The text span the citation refers to
       cited_text = final_report[citation.start_index:citation.end_index]
       print(f"Citation {i+1}:")
       print(f"  Cited Text: {cited_text}")
       print(f"  Title: {citation.title}")
       print(f"  URL: {citation.url}")
       print(f"  Location: chars {citation.start_index}–{citation.end_index}")

print("\n" + "="*50 + "\n")

## --- INSPECT INTERMEDIATE STEPS ---
print("--- INTERMEDIATE STEPS ---")

## 1. Reasoning Steps: Internal plans and summaries generated by the model.
try:
   reasoning_step = next(item for item in response.output if item.type == "reasoning")
   print("\n[Found a Reasoning Step]")
   for summary_part in reasoning_step.summary:
       print(f"  - {summary_part.text}")
except StopIteration:
   print("\nNo reasoning steps found.")

## 2. Web Search Calls: The exact search queries the agent executed.
try:
   search_step = next(item for item in response.output if item.type == "web_search_call")
   print("\n[Found a Web Search Call]")
   print(f"  Query Executed: '{search_step.action['query']}'")
   print(f"  Status: {search_step.status}")
except StopIteration:
   print("\nNo web search steps found.")

## 3. Code Execution: Any code run by the agent using the code interpreter.
try:
   code_step = next(item for item in response.output if item.type == "code_interpreter_call")
   print("\n[Found a Code Execution Step]")
   print("  Code Input:")
   print(f"  ```python\n{code_step.input}\n  ```")
   print("  Code Output:")
   print(f"  {code_step.output}")
except StopIteration:
   print("\nNo code execution steps found.")

This code snippet utilizes the OpenAI API to perform a “Deep Research” task. It starts by initializing the OpenAI client with your API key, which is crucial for authentication. Then, it defines the role of the AI agent as a professional researcher and sets the user’s research question about the economic impact of semaglutide. The code constructs an API call to the o3-deep-research-2025-06-26 model, providing the defined system message and user query as input. It also requests an automatic summary of the reasoning and enables web search capabilities. After making the API call, it extracts and prints the final generated report.

此代码片段利用 OpenAI API 执行”DeepResearch”任务。它首先使用您的 API 密钥初始化 OpenAI 客户端，这对于身份验证至关重要。然后，它将 AI智能体角色定义为专业研究员，并设置用户关于司美格鲁肽经济影响的研究问题。代码构造对 o3-deep-research-2025-06-26 模型的 API 调用，提供定义的系统消息和用户查询作为输入。它还请求推理的自动摘要并启用网络搜索功能。进行 API 调用后，它提取并打印最终生成的报告。

Subsequently, it attempts to access and display inline citations and metadata from the report’s annotations, including the cited text, title, URL, and location within the report. Finally, it inspects and prints details about the intermediate steps the model took, such as reasoning steps, web search calls (including the query executed), and any code execution steps if a code interpreter was used.

随后，它尝试访问并显示报告注释中的内联引用和元数据，包括引用文本、标题、URL 和报告中的位置。最后，它检查并打印模型采取的中间步骤的详细信息，例如推理步骤、网络搜索调用（包括执行的查询）以及如果使用了代码解释器的任何代码执行步骤。

At a Glance

概览

What: 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. This makes it difficult to break down high-level objectives into a manageable series of smaller, executable tasks. Consequently, the system fails to strategize effectively, leading to incomplete or incorrect results when faced with intricate goals.

是什么： 复杂问题通常无法通过单一行动解决，需要远见卓识来实现预期结果。如果没有结构化方法，智能体系统难以处理涉及多个步骤和依赖关系的多方面请求。这使得将高层目标分解为一系列可管理的较小可执行任务变得困难。因此，系统无法有效制定策略，在面对复杂目标时会导致结果不完整或不正确。

Why: 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. This allows the system to manage complex workflows, orchestrate various tools, and handle dependencies in a logical order. LLMs are particularly well-suited for this, as they can generate plausible and effective plans based on their vast training data. This structured approach transforms a simple reactive agent into a strategic executor that can proactively work towards a complex objective and even adapt its plan if necessary.

为什么： 规划模式通过让智能体系统首先创建一个连贯的计划来解决目标，提供了标准化解决方案。它涉及将高层目标分解为一系列更小的可操作步骤或子目标。这允许系统以逻辑顺序管理复杂工作流、编排各种工具并处理依赖关系。LLM 特别适合这一点，因为它们可以基于庞大的训练数据生成合理且有效的计划。这种结构化方法将简单的反应性智能体转变为战略执行者，可以主动朝着复杂目标努力，甚至在必要时调整其计划。

Rule of thumb: 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, onboarding a new employee, or executing a competitive analysis. Apply the Planning pattern whenever a task requires a sequence of interdependent operations to reach a final, synthesized outcome.

经验法则： 当用户的请求太复杂而无法通过单个操作或工具处理时使用此模式。它非常适合自动化多步流程，例如生成详细的研究报告、新员工入职或执行竞争分析。每当任务需要一系列相互依赖的操作以达到最终的综合结果时，应用规划模式。

Visual summary

Fig.4; Planning design pattern

可视化摘要

图 4：规划设计模式

Key Takeaways

关键要点

• Planning enables agents to break down complex goals into actionable, sequential steps.

• 规划使智能体将复杂目标分解为可操作的顺序步骤。

• It is essential for handling multi-step tasks, workflow automation, and navigating complex environments.

• 它对于处理多步任务、工作流自动化和导航复杂环境至关重要。

• LLMs can perform planning by generating step-by-step approaches based on task descriptions.

• LLM 可以通过基于任务描述生成逐步方法来执行规划。

• Explicitly prompting or designing tasks to require planning steps encourages this behavior in agent frameworks.

• 明确提示或设计任务以要求规划步骤会在智能体框架中鼓励这种行为。

• Google Deep Research is an agent analyzing on our behalf sources obtained using Google Search as a tool. It reflects, plans, and executes

• Google Deep Research 是一个代表我们分析使用 Google 搜索作为工具获得的来源的智能体。它进行反思、规划并执行。

Conclusion

结论

In conclusion, the Planning pattern is a foundational component that elevates agentic systems from simple reactive responders to strategic, goal-oriented executors. Modern large language models provide the core capability for this, autonomously decomposing high-level objectives into coherent, actionable steps. This pattern scales from straightforward, sequential task execution, as demonstrated by the CrewAI agent creating and following a writing plan, to more complex and dynamic systems. The Google DeepResearch agent exemplifies this advanced application, creating iterative research plans that adapt and evolve based on continuous information gathering. Ultimately, planning provides the essential bridge between human intent and automated execution for complex problems. By structuring a problem-solving approach, this pattern enables agents to manage intricate workflows and deliver comprehensive, synthesized results.

总之，规划模式是将智能体从简单的反应性响应者提升为战略性、目标导向的执行者的基础组件。现代大型语言模型为此提供了核心能力，自主地将高级目标分解为连贯的可操作步骤。此模式从简单的顺序任务执行（如 CrewAI智能体遵循写作计划所演示的）扩展到更复杂和动态的系统。Google DeepResearch智能体展示了高级应用，创建基于持续信息收集而适应和演化的迭代研究计划。最终，规划为复杂问题的人类意图和自动化执行之间提供了必要的桥梁。通过构建问题解决方法，此模式使智能体能够管理工作流并提供全面的综合结果。

References

参考文献

1. Google DeepResearch (Gemini Feature): gemini.google.com
2. OpenAI ,Introducing deep research https://openai.com/index/introducing-deep-research/
3. Perplexity, Introducing Perplexity Deep Research, https://www.perplexity.ai/hub/blog/introducing-perplexity-deep-research