🤝 Multi-Agent Systems
While single agents handle basic tasks, complex workflows (e.g., software engineering, comprehensive market research) are best resolved by a system of specialized agents working together.
Multi-agent architectures divide complexity, specialize models, and scale processing.
🆚 Single Agent vs. Multi-Agent
| Attribute | Single Agent | Multi-Agent System |
|---|---|---|
| Context Load | Must hold instructions for every subtask in one prompt. | Each agent has a specialized, smaller system prompt. |
| Model Selection | Forced to use one generalist model (e.g., GPT-4o). | Can mix models (e.g., cheap models for parsing, smart models for reasoning). |
| Error Isolation | If one step fails, the entire session can crash or loop. | Failures can be isolated and retried within a single agent’s scope. |
| Execution | Purely sequential. | Can run multiple subtasks in parallel. |
🏗️ Multi-Agent System Architectures
1. 🎯 Supervisor (Orchestrator-Workers)
A single “supervisor” agent decomposes the user request, delegates subtasks to specialist workers, collects their outputs, and synthesizes the final result.
- Best For: Open-ended tasks requiring planning and dynamic task delegation.
2. 🔗 Pipeline Pattern (Sequential Chain)
Agents execute tasks in a linear sequence, where the output of one agent becomes the input for the next.
[Scraper Agent] ──► [Structured Parser] ──► [Analysis Agent] ──► [Reporter Agent]- Best For: Structured data pipelines, document generation, and ETL.
3. 🌐 Peer-to-Peer (Decentralized Network)
Agents interact as equals without a central supervisor. They pass tasks and communicate via a shared message bus.
- Best For: Collaborative design, game simulations, or negotiation workflows.
4. 🔁 Debate & Reflection Pattern
Specialist agents with opposing goals critique each other’s outputs. For example, a Generator Agent writes code, and a Critique Agent reviews it for security flaws and sends feedback back to the Generator.
┌───────────────┐ code ┌──────────────┐
│ Generator │───────────────────►│ Critique │
│ Agent │◄───────────────────│ Agent │
└───────────────┘ feedback └──────────────┘- Best For: High-stakes tasks requiring strict quality control (e.g., code review, smart contract audits).
📡 Agent Communication Protocols
| Protocol | Mechanism | Best For | Examples |
|---|---|---|---|
| Shared Graph State | Agents read and write to a centralized, shared state object. The system router determines the execution path based on state transitions. | Complex, custom business logic and state machines. | LangGraph |
| Message Queues | Agents act as distinct microservices communicating asynchronously by pushing/polling JSON payloads from a queue. | Production-grade microservices and multi-process architectures. | RabbitMQ, Redis |
| Model Context Protocol (MCP) | Standardized client-server protocol enabling secure, uniform sharing of prompts, resources, and tools. | Inter-agent tool sharing and open ecosystems. | MCP Specification |
🛠️ Multi-Agent Frameworks
| Framework | Core Paradigm | Best Suited For |
|---|---|---|
| LangGraph | Cyclic state graphs, fine-grained control | Complex, custom business logic and state machines. |
| CrewAI | Role-based, sequential tasks | Standard business processes (e.g., content marketing, research). |
| AutoGen | Conversational, event-driven | Multi-agent conversations, simulation, and planning. |
| Swarm (OpenAI) | Lightweight handoffs and routines | Simple routing and educational prototypes. |