AI Agents & Automation

Multi-Agent Systems

When one objective requires genuinely different kinds of work — extraction, policy checking, drafting — that degrade each other when combined into a single agent.

Specialization is the argument for the pattern: an agent built for document extraction and one built for policy checking need different tools, prompts, and evaluation criteria. Combining them makes both worse.

The cost is coordination. Failures in multi-agent systems are usually failures of handoff rather than of individual reasoning, which is why observability across the whole run matters more than the accuracy of any single step.

What the work includes

  • Decomposition of an objective into agents with distinct responsibilities
  • Coordination covering planning, delegation, and reconciliation of results
  • Observability over the whole run, including each agent's intent
  • Checkpoints placed before irreversible steps rather than only at the start
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// Common Questions

Questions
About This

Common questions about Multi-Agent Systems.

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When a single objective requires genuinely different kinds of work — extraction, policy checking, drafting — that benefit from different models, tools, or permissions. If one agent with one toolset covers the task, adding more only adds coordination cost.

Through explicit boundaries: each agent has a defined responsibility, its own permissions, and a contract for what it passes on. Most multi-agent failures come from ambiguity about which agent owns a decision, not from the individual agents performing badly.

It can, since more steps mean more model calls. It can also cost less, because narrow tasks can be routed to smaller models while only the genuinely hard steps reach an expensive one. Which way it goes depends on how deliberately the routing is designed.

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