代理 AI 架构

代理 AI 架构是对“这是什么样的系统？”的结构性回答。单独的 LLM 是一个生成器；而代理是一个决定下一步做什么的生成器，保持状态，与工具交互，交接给其他代理，并且足够可观察，以便人类可以进行干预。架构是一组承诺，使所有这些事情能够发生而不发生冲突。

这些承诺来自模式，而不是单一的图表。监督者与协调者-工作者的架构与群体并不相同；带有步骤预算和决策日志的 ReAct 循环与计划后执行并不相同。目录为您提供了精确区分的词汇 — 并在利益相关者询问“你为什么这样构建？”时为其辩护。

Field-tested patterns to start with

• Supervisor — Place a coordinating agent above a set of specialised agents and route work to them.
• Orchestrator-Workers — An orchestrator dynamically breaks a task into subtasks at runtime and delegates each to a worker LLM, then synthesises results.
• Augmented LLM — Build the foundational agent block as an LLM augmented with retrieval, tools, and memory that the model actively chooses to use, rather than a bare-model call.
• ReAct — Interleave a single thought, a single tool call, and a single observation per step so the agent reasons over fresh evidence.
• Plan-and-Execute — Plan all the steps once with a strong model, then execute each step with a cheaper model under the plan.
• Handoff — Transfer the active conversation from one agent to another, carrying context across the switch.
• Subagent Isolation — Run subagents in isolated workspaces so their writes do not collide and parallelism is safe.
• Decision Log — Persist the agent's reasoning trace alongside its actions so post-hoc review can explain why.
• Agent Resumption — Persist agent execution state so a long-running run survives restarts, deploys, or user disconnects.
• Step Budget — Cap the number of tool calls or loop iterations the agent is allowed within a single request.

Recommended reading

• Planning & Control Flow — 45 patterns
• Multi-Agent — 50 patterns
• Governance & Observability — 46 patterns
• Tool Use & Environment — 51 patterns

Or open the full contents for all 562 patterns in 14 books.

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