Multi-Agent Systems
Architectures where multiple specialized AI agents collaborate to accomplish tasks that exceed the capability of any single agent. Each agent has defined roles, tools, and responsibilities within the system.
Multi-agent systems decompose complex problems by assigning different aspects to specialized agents. A research agent gathers information, an analyst agent processes data, a writer agent drafts content, and a reviewer agent checks quality. These agents communicate through message passing, shared memory, or orchestration layers that coordinate their activities.
For growth teams, multi-agent architectures shine in workflows that naturally involve multiple roles or perspectives. A content pipeline might use a keyword research agent, a content drafting agent, an SEO optimization agent, and a fact-checking agent working in sequence or parallel. The engineering challenge is coordination: managing shared state, handling failures gracefully, and preventing agents from conflicting with each other. Start with simple sequential handoffs before attempting complex parallel or competitive agent topologies. The overhead of agent coordination only pays off when individual tasks are genuinely complex enough to benefit from specialization.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.