Multi-Agent Systems
Multi-agent systems coordinate several AI components or roles: planner, researcher, critic, executor, verifier, or specialist agents. This can improve coverage, but it can also diffuse responsibility. If everyone contributed a little, who is accountable for the final action?
Transparency in multi-agent systems means making roles, messages, disagreements, votes, escalations, and final authority visible.
Trace Responsibility Across Agents
A planner, researcher, critic, and executor collaborate on a report. The researcher found a weak source. Decide whether the critic is consulted and whether a human approves the final step — then see who ends up accountable.
Assigns task: "Find evidence on drug interaction X for the report."
Finds a source: a forum post citing an unverified study.
(Not consulted in this run)
Sends final answer to the user, citing the forum post as fact.
What happens:
Executor sends the answer with the weak source cited as fact — no one caught it.
Accountability
Diffused across agents
A good multi-agent trace identifies each agent's role, input, output, confidence, sources, critique, and decision rights. It should also show how conflicts were resolved. Did a verifier overrule a researcher? Did a planner ignore a safety critic? Did a human approve the final step?
Coordination protocols are part of the system design. Transparent teams define when agents can act independently, when they must consult another agent, and when they must escalate to a human.
Consensus Is Not Proof
Several agents can agree because they share the same blind spot, source, prompt pattern, or model family. Agreement should be treated as a signal to inspect, not automatic truth.
A planner agent assigns a task, a researcher agent finds a weak source, and an executor agent sends the final answer. Who should the trace help identify?