Multi-agent systems

Multi-agent systems are architectures in which several autonomous AI agents interact and coordinate to accomplish a task or solve a problem beyond the capacity of a single agent. Unlike single-agent agentic AI, where one agent orchestrates an entire process, a multi-agent system decomposes the task into parallel or sequential sub-operations assigned to specialized agents: one agent retrieves information from databases, another synthesizes the results, a third validates consistency, a fourth drafts the final deliverable. Orchestration can be centralized (a master agent directs the others) or decentralized (agents negotiate directly with one another). This architecture improves performance on long, complex and modular tasks, and allows the context limits of language models to be overcome. However, it introduces specific and poorly controlled risks. The multiplication of exchanges between agents widens the attack surface: an indirect prompt injection targeting a single agent can compromise the entire processing chain. Emergent behaviors arising from coordination are difficult to anticipate and test. Decision traceability becomes opaque, because no individual agent can account for the overall reasoning. For insurance, multi-agent systems raise novel questions of liability attribution, governance and explainability: in the event of a loss caused by a collective decision of a multi-agent system, the causal chain may prove impossible to reconstruct, which complicates establishing fault and activating cover.

An insurer deploys a multi-agent system for automated claims analysis: one agent extracts facts, a second consults the policy, a third assesses cover, a fourth proposes an indemnity amount. If the proposal is wrong, no individual agent is responsible for the overall error, making audit and correction far more complex than in a centralized system.

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