Five Core AI Agent Paradigms: Reflection, Tool Use, ReAct, Planning, and Multi‑Agent Collaboration

AI agents are rapidly evolving, and five core agentic patterns describe how large language models transition from simple generators to collaborative systems.

Reflection Pattern

Process

用户Query
   │
   ▼
LLM（Generate）
   │
   ▼
初步输出（Initial output）
   │
   ▼
LLM（Reflect）
   │
   ▼
反思输出（Reflected output）
   │
   └───> 若需改进，迭代回Generate或Reflect
   ▼
最终输出（Response）

Key Points

Self‑reflection and metacognition : the model critiques its own initial answer.

Dual‑system analogy : fast System 1 corresponds to Generate, slow System 2 to Reflect.

Scientific‑method mapping : hypothesis → experiment → critique → revision cycle.

Tool Use Pattern

Process

用户Query
   │
   ▼
LLM（分析任务/决定是否调用工具）
   │
   ├─────────────┐
   │             │
   ▼             ▼
（无需工具）   （需要工具）
   │             │
   ▼             ▼
直接生成       工具调用（向量库、API等）
   │             │
   ▼             ▼
生成最终响应   获取工具结果后生成最终响应
   │
   ▼
Response

Key Points

The model first assesses whether its internal knowledge suffices; if not, it invokes external tools such as vector stores or APIs.

Tool invocation extends the agent’s effective memory and capabilities beyond the model’s training cutoff.

Results from tools are integrated with the model’s own reasoning to produce the final answer.

ReAct (Reason‑Action) Pattern

Process

用户Query
   │
   ▼
LLM（Reason）
   │
   ▼
决定行动（Action） → 调用工具/与环境交互
   │                 │
   ▼                 ▼
Environment          │
   │                 │
   └───Result───────┘
   │
   └───> 结果反馈，进入新一轮 Reason
   ▼
LLM（Generate）
   ▼
Response

Key Points

The loop couples reasoning with concrete actions, allowing the agent to observe environmental feedback.

Supports trial‑and‑error learning: reason → act → observe → revise.

Enables embodied or interactive intelligence for robotics, web‑search, and other dynamic tasks.

Planning Pattern

Process

用户Query
   │
   ▼
Planner（任务规划器）
   │
   ▼
任务分解（Generated tasks）
   │
   ▼
单个任务执行（Execute single task）
   │
   ▼
ReAct Agent（执行与反馈）
   │
   ▼
结果（Results）
   │
   ▼
Planner判断是否完成（Finished?）
   │      ├──YES──► 生成最终Response
   │      └──NO──► 继续分解/调整任务，循环执行

Key Points

Complex goals are decomposed into manageable sub‑tasks, enabling a divide‑and‑conquer strategy.

The planner monitors sub‑task results and can dynamically adjust the plan.

Iterative refinement yields progressive intelligence and goal‑directed optimization.

Multi‑Agent Pattern

Process

用户Query
   │
   ▼
PM agent（项目经理）
   │
   ├─────────────┐
   │             │
   ▼             ▼
Tech lead agent   DevOps agent
   │             │
   ▼             ▼
SDE agent<─┘
   │
   ▼
（多智能体间委派、协作、反馈）
   ▼
PM agent整合结果
   ▼
Response

Key Points

Agents specialize (project manager, tech lead, DevOps, developer) and cooperate to solve complex tasks.

Decentralized autonomy provides resilience and adaptability.

Collective intelligence emerges from delegation, information sharing, and feedback loops.

Overall Insight

These five patterns—Reflection, Tool Use, ReAct, Planning, and Multi‑Agent—constitute the primary architectural motifs for building AI systems that can self‑critique, extend their knowledge, interact with environments, decompose goals, and collaborate as distributed teams.