When multiple AI agents collaborate on tasks, the orchestration layer becomes a critical attack surface. A compromised orchestrator can redirect agent behavior, inject malicious instructions, or exfiltrate data through inter-agent channels. Securing this layer requires deliberate architectural choices.
In the broker pattern, no agent communicates directly with another. All messages pass through a central broker that validates message format, checks authorization, and logs every exchange. The broker enforces that Agent A can only send specific message types to Agent B, preventing lateral movement if one agent is compromised.
rules:
- action: "message.send"
from: "research-agent"
to: ["summary-agent"]
allowed_types: ["research.result"]
effect: "allow"
Agents execute in a strict sequence. Each agent receives input only from its predecessor and passes output only to its successor. This limits the blast radius of any single compromised agent to one step in the chain. Authensor policies can enforce pipeline ordering by validating the source_agent field on every action envelope.
A dedicated supervisor agent monitors all subordinate agents. It validates outputs before they propagate, enforces rate limits, and can terminate agents that exhibit anomalous behavior. The supervisor itself should be minimal in scope with no tool access beyond monitoring and control APIs.
No single pattern is sufficient. Production deployments should combine broker-level message validation, pipeline-level ordering enforcement, and supervisor-level behavioral monitoring. Each layer catches failures that slip through the others.
Authensor's policy engine supports all three patterns through action-level rules, principal binding, and Sentinel behavioral monitoring. Define trust boundaries in policy, enforce them at the orchestration layer, and verify compliance through audit trails. The result is a multi-agent system where compromise of one agent does not automatically compromise the whole.
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