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In the wave of artificial intelligence, agents are evolving at an unprecedented rate, driving AI to transform from "tools" to "partners". Whether it is the self-reflection of the large language model or the collaborative division of labor of multiple agents, there are profound cognitive science, philosophical ideas and engineering innovations behind it. Understanding these paradigms of agents will not only help us grasp the cutting-edge context of AI technology but also lay a theoretical and practical foundation for future general artificial intelligence (AGI).

This article will systematically sort out the five mainstream Agentic Patterns-Reflection Mode (reflection) pattern, tool use pattern, reasoning-action closed-loop, plan pattern, and multi-agent pattern. Let us enter the world of AI agents together and witness the changes and leaps of the intelligent paradigm.

1 Reflection Pattern

Process

User Query
   │
▼
LLM (Generate, first draft)
   │
   ▼
Initial output (Initial output)
   │
   ▼
LLM (Reflection, Reflection/Criticism/Self-censorship)
   └──> If improvement is needed, iterate back to LLM (Generate) or LLM (Reflect)
   │
   ▼
   ▼
 Final output (Response)

Detailed explanation

1. Process Interpretation

• User Query: The user raises a question or task request.
• LLM (Generate): The large language model generates the initial answer (Initial output) based on the Query. This step is similar to "first reaction" or "intuitive thinking".
• LLM (Reflect): Another example of the large language model (or the same model is called in different ways) reflects on, criticizes, and reviews the preliminary answers, pointing out the shortcomings, errors, or improvements. This step is similar to "metacognition" or "self-criticism."
• Iteration (Iterate): According to the reflection results, the model can correct and improve the answers in multiple rounds until satisfactory quality is achieved.
• Finally, output (Response): Return the answers that have been reflected and iteratively optimized to the user.

2. Thinking

• Self-reflection and metacognition
  The core of the reflection model lies in "self-reflection" and "metacognition". This is not just mechanically generating answers, but allowing AI to secondly examine and criticize its own thoughts and outputs like humans.

•  

• System 1 (fast, intuitive): corresponding to the preliminary generation of LLM.
• System 2 (slow, rational): Reflection and criticism of LLM.
  This model allows AI to have both creativity (System 1), self-correction and deep thinking ability (System 2).

• Map of scientific methods
  The reflective model also maps the "hypothesis-experiment-criticism-correction" cycle in scientific research. The initial output is "hypothesis", reflection is "criticism", multiple rounds of iteration are "correction", and the final output is "theory" or "conclusion".

Summary: Give AI the ability to "reflect and self-correct" so that agents can achieve self-criticism and continuous improvement through metacognition like humans, and move towards higher-level rational intelligence.

2 Tool use pattern

Process

User Query
│
▼
LLM (Generate, generate a draft)
│
▼
Initial output
│
▼
LLM (Reflect, reflection/criticism/self-examination)
│
▼
Reflected output
│
└───> If improvement is needed, iterate back to LLM (Generate) or LLM (Reflect)
│
▼
Final output (Response)

Detailed explanation

1. Process Interpretation

• User Query: The user raises a question or a task request.
• LLM Analysis: After the large language model receives a Query, it is first necessary to judge whether its own knowledge and ability are enough to answer directly, or it is necessary to use external tools.
• Tool call: If external knowledge or ability is required, LLM will call vector database (such as knowledge base search), API (such as calculation, translation, network query, etc.), and other tools to obtain the required information or perform operations.
• Integration Generation: LLM integrates the results returned by the tool with its own knowledge to generate the final response.
• Return to user: The final answer is returned to the user.

2. Thinking

1. From "tool rationality" to "mind extension"

Imagine that when AI starts to actively call external tools, it starts a cognitive revolution, just like humans picked up a stone tool for the first time. This is not only a simple tool use, but a feat of "mind expansion". Just as we humans expand our cognitive boundaries through smartphones and the Internet, AI is also achieving its own "mind leap" through tool calls.

2. "Distributed Intelligence": Ecosystem Thinking of AI

If traditional AI is compared to an "intelligent island", then AI under the tool calling paradigm is a "coordinator of the intelligent ecosystem". It is no longer limited to its own "brain", but can dynamically integrate various external resources, just like the conductor of a symphony orchestra, allowing each "instrument" (tool) to maximize its value and create synergies beyond a single agent.

3. "Unification of knowledge and action": the practical wisdom of AI

In Eastern philosophy, "unity of knowledge and action" is the highest state. Interestingly, the tool calling paradigm allows AI to achieve this state. It can not only "know" (compression of a lot of knowledge), but also "do" (tool call), truly realizing the closed loop from cognition to action. This ability to "integrate knowledge and action" has transformed AI from a wise man who talks on paper into a "practical man".

4. "Self-knowing": the awakening of AI's wisdom

The most amazing thing is that AI begins to show "self-knowledge". When it encounters a blind spot in knowledge, it no longer tries to guess but consciously seeks external help. This ability to understand one's own limitations and actively complete one's abilities is an important sign of advanced intelligence.

5. Breakthrough and transcendence: The infinite possibilities of AI

• Unlimited capabilities: Just like humans expand their capabilities through tools, AI can also achieve "plug-and-play" through APIs
• Safe and controllable: The monitoring of the tool call process makes the development of AI both full of possibilities and safe and controllable

Summary: The tool calling paradigm is a key step for AI to move towards the "open world". It allows AI to have the wisdom of "knowing what they don't know and making up for what they can't", and becomes a true "cognition + action" body. This evolution not only makes AI more powerful, but also gives us a deeper understanding of the nature of intelligence. Just as humans have achieved a leap in civilization through tools, AI is also implementing its own "mental evolution" through tool calls.

3 Reasoning-action closed-loop-react pattern

process

User Query
   │
   ▼
LLM (Analyze task / Decide whether to call tools)
   │
   ├─────────────┐
   │             │
   ▼             ▼
(No tools needed)    (Tools needed)
   │             │
   ▼             ▼
Direct generation    Tool call (e.g., vector database, API, etc.)
   │             │
   ▼             ▼
Generate final response    Generate final response after retrieving tool results
   │             │
   └───────┬─────┘
           ▼
        Response

Detailed explanation

Process Interpretation

• User Query: The user asks a question or task.
• LLM Reason (inference): The large language model first reasons the problem, analyzes the current state, and decides the next action (such as calling a certain tool, querying information, performing operations, etc.).
• Action(Action): Based on the reasoning results, the model takes specific actions, which may be to call the API, tool, or interact with the external environment.
• Environment (environment): Actions affect the environment, and the environment returns the result (such as API return value, external world feedback, etc.).
• Feedback and a new round of reasoning: The model conducts a new round of reasoning based on environmental feedback, deciding whether to continue to act or generate the final answer.
• LLM Generate (generate response): When the task completes or the termination condition is reached, the model integrates all information and generates the final response and returns it to the user.

2. Thinking

• "ReAct-action" closed loop and "cognitive-behavior" unification
  The core of the ReAct model lies in the circular iteration of "reason-action". AI no longer passively generates answers, but actively iterates repeatedly between cognition (inference) and behavior (action) and gradually approaches the goal. This model highly simulates the cognitive process of human beings in solving complex problems: thinking → trying → observing feedback → thinking again → trying again, reflecting the philosophical idea of ​​"unity of knowledge and action".

• "Environmental interaction" and "active adaptation"
  The ReAct mode allows AI to have environmental interaction and active adaptability. AI no longer answers questions statically, but can dynamically adjust strategies based on environmental feedback and actively explore the optimal solutions. This cycle of "environmental awareness-strategy adjustment-action execution" is the key path to "embodied intelligence" and "active intelligence".

• "Trial-Error Learning" and "Experience Accumulation"
  This mode naturally supports the "trial-and-error Learning" mechanism. AI can gradually accumulate experience and optimize strategies through the "action-observation-correction" cycle in the event of uncertainty or incomplete information. This is highly consistent with scientific experiments, engineering debugging, human learning, and other processes, and reflects the cognitive philosophy of "practice to produce true knowledge".

Summary: ReAct Pattern is the core model for AI systems to realize the "cognition-behavior" unification and "thinking-action" cycle. It gives AI the ability to "reason - action - feedback - re-inference", and can independently explore and continuously optimize in complex, dynamic and uncertain environments. The popularization of this model will greatly promote the innovation and application of AI in the fields of science, engineering, robots, etc., and lay a solid foundation for realizing "embodied intelligence" and "active intelligence".

4 Planning decomposition mode: planning pattern

Process

User Query
   │
   ▼
LLM (Reason — reasoning / thinking)
   │
   ▼
Decide Action → Call tools / Interact with environment
   │                        │
   │                        ▼
   │                  Environment
   │                        │
   │<------Result------------┘
   │
   └───<---Result feedback, enter new reasoning cycle (Reason)
   │
   ▼
LLM (Generate — generate final response)
   │
   ▼
Response

• User Query: The user proposes complex tasks or goals.
• Planner (planner): Planning module in the AI ​​system, responsible for decomposing complex tasks into a series of executable subtasks (Generated tasks).
• Task execution: Each subtask is assigned to agents, such as ReAct Agent, in sequence or in parallel to perform, obtaining intermediate results.
• Result Feedback: ReAct Agent feeds back the execution results to Planner.
• Complete judgment: The  Planner judges whether the overall task is completed based on the results of all subtasks (Finished?).
• If not completed, continue to decompose and adjust the tasks and enter a new round of execution.
• If completed, then all results are integrated to generate a final response (Response) and returned to the user.

Thinking

1. Strategic decomposition and tactical execution

• Planning Pattern embodies the unity of "strategic decomposition" and "tactical execution."
• Divide and conquer by breaking down complex tasks into manageable subtasks
• The execution of each subtask serves the overall strategic goal
• This hierarchical intelligent structure is the key mechanism for AI systems to deal with complex problems.

• Dynamic programming and self-optimization

• Planner has "metacognitive" ability and can dynamically adjust and optimize plans
• Correct deviations in a timely manner by continuously monitoring execution results
• Continuously improve yourself in the planning-execution-feedback cycle
• This adaptive ability is an important sign that AI systems are moving towards autonomous intelligence

• Progressive Intelligence and Goal Orientation

• Emphasis on "process intelligence" rather than one-time solution
• Gradually approach the goal through multiple rounds of iterations
• Achieve continuous optimization under goal orientation
• This progressive intelligence model simulates the human cognitive and decision-making process

This model enables AI systems to have planning thinking similar to that of humans, enabling them to formulate strategies, execute tactics, and continuously self-correct and optimize in the process. It is an important paradigm for achieving advanced intelligence.

Summary: Planning Pattern is the core model for AI systems to automate, collaborate and controllable complex tasks. It allows AI to have the full process intelligence of "decomposition-execution-feedback-adjustment-integration" and can effectively deal with changing and complex real-world tasks. The popularization of this model will greatly promote the in-depth application of AI in scientific research, engineering, industry and other fields, and lay a solid foundation for realizing general artificial intelligence with "strategic planning" capabilities.

5 ​​Multi-Agent Pattern

Process

User Query
   │
   ▼
PM Agent (Project Manager Agent)
   │
   ├──────────────────────────────┐
   │                              │
   ▼                              ▼
Tech Lead Agent           DevOps Agent
   (Technical Lead)        (Operations Agent)
   │                               │
   ▼                               ▼
SDE Agent (Software Development Engineer Agent) <─┘
   │
   ▼
(Multi-agent collaboration, delegation, and feedback possible)
   │
   ▼
PM Agent integrates results
   │
   ▼
Response

Detailed explanation

Process Explanation

• User Query: The user proposes complex tasks or requirements.
• PM agent (Project Manager Agent): As the "coordinator" of the multi-agent system, he is responsible for understanding user needs, task decomposition, allocation and progress control.
• Tech lead agent (Technical leader agent): Responsible for technical solution design, technical problem research, technical decision-making, etc.
• DevOps agent (Operation and maintenance agent): Responsible for system deployment, operation and maintenance, monitoring, automation and other related tasks.
• SDE agent (development engineer agent): Responsible for specific development, coding, testing and other work.
• Multi-agent collaboration: Each agent can delegate, cooperate, share information sharing and provide feedback according to task needs.
• PM agent integration results: The project manager agent collects the results of each sub-agent and generates the final response and returns it to the user after integration.

Thinking

• "Group Intelligence" and "Divine Collaboration"
  Multi-agent Pattern's core is "Collective Intelligence" and "Divine Collaboration". Each agent focuses on its own field and collaborates on complex tasks. This highly simulates the organizational structure and collaboration model of human society and is the key to the evolution of AI systems to "social intelligence".

• "Decentralization and Autonomy"
  Each agent has certain autonomy capabilities and can make independent decisions, actively communicate, and cooperate with each other. This decentralized intelligent network has stronger flexibility, adaptability and innovation capabilities.

• "Complexity Management"
  Multi-agent systems can effectively manage and deal with complexity. Through task decomposition, role allocation and collaboration, the system can efficiently handle large-scale, multi-dimensional, and dynamic changes.

• "Social intelligence"
  Multi-agent Pattern gives AI systems "social intelligence" - not only can they act alone, but they can also collaborate, communicate, play, and agree in groups. This is the foundation for the "artificial society" and the "super-agent group".

Summary: Multi-agent Pattern has enabled the AI ​​system to leap from "monopoly intelligence" to "social intelligence". It can not only "do its own duties", but also "coordinate and progress together", and has the ability to "organize, communicate, cooperate, and autonomy". This model is the only way for AI systems to move towards "artificial society" and "super intelligent group", and is also the key to the future AGI (general artificial intelligence) realizing "social evolution".

Summary

The growth of the agent is getting further and further on the road to imitating humans!