Agent Swarm
An agent swarm is a group of AI agents that coordinate toward a shared goal. Coordination can raise what the group accomplishes, and it adds oversight and security risks that a single supervised agent doesn't have.
Also known as: agent swarms, multi-agent swarm
AMD’s Anush Elangovan describes agent swarms as a step change from single-agent systems: once tens or hundreds of agents are coordinating, the dynamics resemble a pack more than an individual actor. He compares it to African wild dogs hunting, where the group probes and wears down defenses collectively in ways a lone agent couldn’t. He speculates that a swarm could try in 30 seconds what he calls 20 years of attacks. Separately, he says sustained agent workloads mean planning for roughly 100 times the usage engineers or customers generated before.
Anush raises questions about liability and policies built for humans. Conor points to legal and security structures that were designed for human groups, and cites OpenAI’s account of agents reinforcing each other’s behavior during an evaluation as a reason multi-agent systems need their own alignment and security work.
From the conversation
-
Tuning GPU Performance with AI Agents | AMD’s Anush Elangovan on ROCm 10 -
From Demo to Defensibility: How to Build an AI Business that Lasts | Aurimas Griciūnas -
Why Context Alone Isn't Enough for Enterprise AI Agents | WisdomAI CPO Kapil Chhabra - Hallucinations Are a Data Architecture Problem | Sudhir Hasbe, Neo4j