RT Avi Chawla (@_avichawla)5 Agentic AI design patterns, explained visually!Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration!The visual depicts the 5 most popular design patterns for building AI Agents.1️⃣ Reflection patternThe AI reviews its own work to spot mistakes and iterate until it produces the final response.2️⃣ Tool use patternTools allow LLMs to gather more information by:- Querying a vector database- Executing Python scripts- Invoki ...

AI Agent Design Patterns - Agentic AI 通过整合自我评估、规划和协作来优化输出 [1] - 报告描述了构建 AI Agents 的 5 种最流行的设计模式 [1] Key Design Patterns - 反思模式：AI 审查自身工作以发现错误并迭代，直到产生最终响应 [1] - 工具使用模式：工具允许 LLM 通过查询向量数据库、执行 Python 脚本、调用 API 等来收集更多信息 [1][3] - ReAct (Reason and Act) 模式：ReAct 结合了反思模式和工具使用模式 [2] - 规划模式：AI 通过细分任务和大纲目标来创建路线图，从而更有效地解决任务 [2] - 多代理模式：有多个代理，每个代理都有特定的角色和任务，每个代理也可以访问工具 [3] Frameworks and Implementation - 像 CrewAI 这样的框架主要默认使用 ReAct 模式 [2] - 在 CrewAI 中，指定 `planning=True` 以使用规划模式 [2]

AI Agent Patterns - Basic responder agents primarily generate text responses [1] - Router pattern agents determine the appropriate path to take based on input [2] - Tool calling agents select and execute specific tools to accomplish tasks [3] - Multi-Agent pattern manages and coordinates multiple agents to achieve a common goal [4] - Autonomous pattern agents operate fully independently without direct human intervention [5]