Multi-Agent System

What is Multi-Agent System?

As AI tasks grow in complexity, single-agent solutions often become insufficient. Multi-agent systems address this by decomposing complex workflows into subtasks handled by specialized agents. A research pipeline might use one agent for data collection, another for analysis, a third for report writing, and a fourth for quality review. Each agent brings specialized capabilities, and the system as a whole achieves more than any individual agent could alone.

Trust in multi-agent systems is significantly more complex than trust in individual agents. The system is only as reliable as its weakest link -- a single poorly-performing agent can compromise the entire pipeline. Moreover, agents in a multi-agent system interact with each other, creating trust dependencies that propagate through the system. Trust assessment must consider not just individual agent scores but the trust characteristics of the composition.

Multi-agent trust also raises questions about accountability. When a multi-agent system produces a bad outcome, which agent is responsible? Effective trust infrastructure must be able to trace outcomes back through the chain of agent interactions to identify where failures occurred, enabling appropriate score adjustments for the responsible agent rather than penalizing the entire system uniformly.

Example

How Signet addresses this

Signet scores each agent in a multi-agent system individually, enabling granular trust assessment at the component level. When multi-agent systems are registered, Signet tracks the composition -- which agents are involved and how they interact. Trust Reports for multi-agent systems include both individual agent assessments and a system-level view that highlights the weakest links and potential trust bottlenecks in the pipeline.