CONCEPT
Multi-Agent AI Systems
Architectures in which multiple AI agents interact — collaborating, competing, or negotiating — to complete tasks no single agent would accomplish. The Overmind is the fictional limit; agent swarms shipping software in 2025 are the tractable early form.
A multi-agent AI system is one in which two or more distinct model instances, often with different roles, prompts, or tools, interact over the course of a task. The interaction may be cooperative (agents passing subtasks), adversarial (agents critiquing each other's outputs), or structured (agents occupying roles in an explicit workflow). Multi-agent systems have moved from research curiosity to deployment reality between 2023 and 2025, in parallel with advances in single-agent long-horizon reasoning. They produce capabilities beyond any single agent's reach but introduce failure modes specific to the multi-agent setting: cascading error, emergent collusion, and accountability dilution.
In The You On AI Field Guide
The theoretical attraction of multi-agent systems runs back at least to Minsky's Society of Mind (1986), which proposed that human intelligence itself is best understood as a society of interacting specialist agents. The near-term practical attraction is different and narrower: a single language-model context is bounded, a single role