[GTM glossary](/glossary.txt) Multi-agent system Contextual Intelligence & AI # Multi-agent system In short A multi-agent system is a setup where several AI agents, each with a defined role, collaborate to complete a larger task. One agent might research, another might write, and a third might check the result. The idea is division of labor. A single agent handed a long, mixed task often loses focus. Splitting it into narrow roles with clear handoffs can make each step easier to control and test. It also adds cost and complexity, so it suits some jobs and not others. ## Common patterns * **Orchestrator and workers:** A coordinating agent splits the task, sends pieces to specialists, and assembles the result. * **Pipeline:** Agents work in sequence, each handing output to the next. * **Reviewer:** One agent produces work and another checks it against rules before it goes out. --- ## Example: outbound research and drafting Say a team wants personalized first emails for a target list. A research agent gathers facts per account. A scoring agent picks the best angle. A writing agent drafts the email. A checking agent verifies each claim against the source and flags anything unsupported. A rep reviews the flagged items and approves the rest. --- ## Multi-agent vs. single agent A single agent is simpler, cheaper, and easier to debug. Move to several agents only when one agent clearly struggles, for example when tasks need different tools, different permissions, or independent checking. --- ## How to start ### Map the workflow first Write the steps a person would take. Each step with a distinct input and output is a candidate role. ### Define handoffs Decide the exact format passed between agents. Vague handoffs cause most failures. ### Add checkpoints Log each step and put a human approval before any message is sent or record changed. --- ## Common mistakes * **Too many agents:** Each extra agent adds cost, delay, and a place for errors to compound. * **No shared source of truth:** Agents working from different data produce conflicting answers. * **Unbounded actions:** Letting agents send or edit without limits or review. --- ## Multi-agent system FAQs **What is a multi-agent system?** A multi-agent system is a group of AI agents with distinct roles that cooperate to finish a larger task. Work is split, handed between agents, and often checked by another agent before a final result is produced. **When should you use a multi-agent system instead of one agent?** Use several agents when a task has clearly separate steps needing different tools, permissions, or independent checking. For simple tasks one agent is cheaper and easier to debug, so start there and split only when it struggles. **What are examples of a multi-agent system in sales?** A common setup has one agent researching accounts, another selecting an angle, another drafting outreach, and another verifying claims against sources. A rep then reviews flagged items and approves the rest before anything is sent. **What are the risks of a multi-agent system?** Errors can compound across handoffs, costs rise with each extra agent, and agents may work from inconsistent data. Reduce risk with clear handoff formats, logging, a shared data source, and human approval before external actions. Share term --- ## More related phrases * [MCP server](/glossary/mcp-server.txt) * [Agentic GTM](/glossary/agentic-gtm.txt) * [AI sales agent](/glossary/ai-sales-agent.txt) ## Similar phrases * [Agentic workflow](/glossary/agentic-workflow.txt) An agentic workflow is a multi-step process in which an AI agent decides the next action, uses tools, and checks its work, often with human approval at set checkpoints. * [Agentic AI](/glossary/agentic-ai.txt) Agentic AI describes AI systems that pursue a goal on their own, planning steps, using tools, and adjusting as they go, rather than only answering a single prompt. * [AI agent](/glossary/ai-agent.txt) An AI agent is software powered by an AI model that takes a goal, uses tools and data to act on it, and decides its own next steps. * [AI SDR (AI SDR)](/glossary/ai-sdr.txt) An AI SDR is software that performs sales development tasks such as finding prospects, writing outreach, and booking meetings, either assisting human reps or running parts of outbound on its own. * [Model Context Protocol (MCP)](/glossary/model-context-protocol.txt) The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external tools and data sources in a consistent way, instead of needing a custom integration for each one.