Topic starter
27/10/2026 7:05 pm
TL;DR: The riskiest moment in a multi-agent workflow is often the handoff, not the model call, according to Protecto’s analysis, because one agent can pass too much sensitive context to another and then receive internal findings back into a customer-facing context. That makes policy enforcement at the gateway more important than embedding controls inside every agent.
Editorial analysis by NHI Mgmt Group, based on content published by Protecto: “Data Policy at the Agent Gateway: Stop 6 Leaky Handoffs”.
Key questions
Q: What breaks when AI agents hand off full case context to another agent?
A: The receiving agent often inherits more sensitive data than its role requires, and that excess can persist in memory, logs, or downstream responses.
Q: Why do agent-to-agent handoffs increase access risk?
A: Agent-to-agent handoffs increase risk because authority can move beyond the original requester and be reissued without the same level of scrutiny.
Q: How should security teams govern sensitive data in multi-agent workflows?
A: Treat each delegation step as a policy decision and apply data minimisation at the transfer layer, not only inside the agent.
Practitioner guidance
- Define handoff policies per agent pair Map each sender-receiver combination to the minimum claim, case, and context fields the receiving role actually needs.
- Separate outbound and inbound controls Write different rules for what a review agent may receive and what a customer-facing agent may receive back from the review path.
- Preserve correlation without identity disclosure Use stable placeholders for cross-case pattern detection while removing direct identifiers and health information from transfer payloads.
Bottom line: The article’s central risk is not an individual AI agent acting badly, but sensitive context moving too freely between agents with different roles.
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