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Agents coordinate decentrally via local rules, messages, and handoffs, allowing the execution path to emerge at runtime. Typical conditions for use: Decentralized exploration is desired; Tasks can be adaptively distributed. The central trade-off: High adaptability is gained at the cost of lower predictability and difficult debugging.
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Swarm is a coordination pattern for agentic multi-agent systems: agents coordinate decentrally via local rules, messages, and handoffs, so the execution path emerges at runtime.
The pattern is defined in the “Multi-Agent Systems” pattern catalog as a peer-agent swarm / distributed agent ecosystem coordination variant. It captures no-center coordination where the next active agent results from local handoff rules. The presentation also places Swarm under a security lens as “Distributed Agent Ecosystem” within the OWASP Agentic Security Initiative, and conceptually contrasts it with the decentralized Blackboard approach by highlighting the coordination substrate (message exchange vs. shared stigmergic state).
Picture Swarm as a sketch without a hub: each agent knows only which peers it is allowed to hand off to. When a message arrives, the current agent uses local rules to decide who should take over next. A handoff transfers control and the relevant context completely (no automatic return); therefore “route” is the runtime trace of observed handoffs. To keep exploration from going out of control, implementations typically bound it with an explicit handoff/recursion limit and stabilize messaging to prevent floods.
The next step is not centrally planned; it is determined by local transitions between agents.
Each agent carries restricted decision logic for what peers it may hand off to and when.
A handoff fully transfers control and relevant context; the initiator does not automatically resume.
The execution path becomes visible as the observed sequence of handoffs rather than as a preplanned graph.
Without limits (e.g., handoff/turn bounds) and without backpressure, flooding or unmanageable execution paths can dominate.
Swarm fits when you want adaptive, decentralized exploration and centralized control would be too rigid. The trade-off is lower predictability: the same input may lead to different execution paths, making failures harder to debug. In practice you need safeguards against message floods (e.g., broadcast budgets/backpressure or lightweight routing) and mechanisms for reproducible behavior (e.g., logging the full message sequence and using a defined turn order with a seed).
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