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Multi-Agent Systems

Definition

Multi-agent systems (MAS) are architectures in which two or more AI agents collaborate to accomplish tasks that would be difficult or impossible for a single agent. Each agent typically has a specialized role, its own set of tools, and defined responsibilities. A coordination mechanism, often an orchestrator agent, manages communication, delegates sub-tasks, resolves conflicts, and synthesizes the collective output.

This approach draws from distributed computing and organizational design principles. Just as a product team has specialists in engineering, design, and research, a multi-agent system assigns different aspects of a problem to agents optimized for those particular tasks.

Why It Matters for Product Managers

Multi-agent systems are becoming a core architectural pattern for AI-powered products. PMs need to understand them because they introduce new design decisions around task decomposition, agent specialization, coordination overhead, and failure handling. Getting these decisions right determines whether the system produces reliable, high-quality results or devolves into unpredictable behavior.

From a strategic perspective, multi-agent architectures allow product teams to build modular AI capabilities that can be recombined for different use cases. A research agent, a writing agent, and an analysis agent can be composed differently depending on the workflow. This modularity accelerates development and makes it easier to improve individual components without rewriting the entire system.

How It Works in Practice

  • Architecture design -- Define the agents needed, their roles, and how they communicate. Common patterns include hierarchical (one orchestrator delegates to workers), peer-to-peer (agents negotiate directly), and pipeline (output of one feeds into the next).
  • Agent specialization -- Each agent is configured with specific instructions, tools, and context relevant to its role, such as a data analyst agent with database access or a writing agent with brand guidelines.
  • Orchestration -- A coordinator manages the workflow, routing tasks to appropriate agents, handling dependencies, and aggregating results.
  • Communication protocol -- Agents share structured messages containing task descriptions, intermediate outputs, status updates, and requests for clarification.
  • Quality assurance -- A reviewer agent or human checkpoint validates the final output before delivery, catching errors that individual agents may have introduced.

    • Common Pitfalls

  • Over-engineering the system with too many agents when a simpler single-agent approach would suffice, adding unnecessary coordination overhead.
  • Insufficient error handling between agents, where one agent's failure cascades unpredictably through the system.
  • Lack of observability into agent-to-agent communication, making debugging and performance optimization extremely difficult.
  • Assuming agents will naturally coordinate well without explicit protocols for conflict resolution and task handoff.

    • Related Concepts

    Multi-agent systems are an extension of Agentic AI principles applied at scale, relying on Function Calling for tool use and AI Safety practices to keep each agent within safe boundaries. Human-in-the-Loop checkpoints are often critical for maintaining quality in multi-agent workflows.

    Related Terms

    Agentic AIFunction CallingLarge Language Model (LLM)Human-in-the-LoopAI Safety

    Frequently Asked Questions

    What are multi-agent systems in product management?+
    Multi-agent systems are AI architectures where several specialized agents work together on a task. In product management, this could mean one agent handling user research analysis, another synthesizing competitive data, and a third drafting a product brief, all coordinated to produce a comprehensive output.
    Why are multi-agent systems important for product teams?+
    Multi-agent systems matter because they enable product teams to automate complex workflows that require multiple types of expertise. Instead of building one monolithic AI tool, teams can compose specialized agents that handle different parts of a process, resulting in higher-quality outputs and more maintainable systems.
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