Multi-agent systems are architectural patterns where multiple AI agents collaborate to complete a single task. This area saw rapid growth in 2025: Gartner reported a 1,445% increase in multi-agent system inquiries from Q1 2024 to Q2 2025. The core driver: single agents face context window limits, cumulative single-point errors, and lack of specialization depth when handling complex long-horizon tasks.
## Core Architecture Patterns
**Orchestrator-Subagent pattern**: a main orchestrator agent handles task decomposition, planning, and coordination, distributing subtasks to specialized subagents for execution, then aggregating results. This is currently the dominant multi-agent architecture; Anthropic’s Claude Agent SDK natively supports this pattern.
**Parallel agents**: multiple agents simultaneously process different task portions, then merge outputs. Suited for parallelizable tasks — analyzing multiple documents simultaneously, concurrent multi-source search.
**Pipeline**: agent A’s output feeds agent B’s input, forming a serial processing chain. Suited for tasks with clear processing stages: “draft → review → revision → formatting.”
**Debate**: multiple agents provide different perspectives or solutions on the same problem; a final agent synthesizes and judges. Improves output reliability and reduces single-model bias.
## Framework Comparison
**AutoGen (Microsoft)**: implements multi-agent conversations via GroupChat, with custom roles (AssistantAgent, UserProxyAgent). Focused on conversational collaboration.
**LangGraph**: graph-based state machine framework modeling agent workflows as directed graphs — nodes are processing steps, edges are conditional transitions. Supports loops and branching logic for complex flow control.
**CrewAI**: emphasizes role-responsibility definitions; YAML configuration describes agent roles, tool permissions, and collaboration patterns, lowering the barrier to multi-agent system construction.
**Claude Agent SDK**: Anthropic’s native SDK with built-in Orchestrator pattern; Claude models as main orchestrators with subagents as different Claude instances or external services.
## Design Considerations
Multi-agent challenges: inter-agent communication latency (0.5–5 seconds per LLM call), information loss in context transfer, and token cost inflation from loop calls. Practical recommendations: define clear input/output specifications per subagent; set maximum iteration counts to prevent infinite loops; add Human-in-the-Loop checkpoints at critical steps.
See [AI Agents Introduction](https://sunqi.org/ai-agent-introduction-en/) and [LangGraph documentation](https://langchain-ai.github.io/langgraph/).
