Signed but Not Safe: Requirement Integrity in Multi-Agent LLM Handoffs
Preregistered study shows multi-agent LLM handoffs silently drop or alter user requirements — a hidden integrity risk for enterprises delegating complex tasks to chained AI sub-agents.
Summary written by editorial AI · Source link below
arXiv:2611.07483v1 Announce Type: new Abstract: Enterprise AI agents increasingly split a user's request across subagents and merge their results, and each handoff can summarize away, alter or ignore a requirement. We report a preregistered study with synthetic, exactly scorable tasks comparing free-text, structured JSON and signed requirement-manifest handoffs: the orchestrator extracts each requirement as a verbatim quote plus a checkable predicate, code signs the manifest with HMAC-SHA256, a
Editorial Analysis
Enterprises building multi-agent AI workflows face a new class of integrity failure where requirements are summarised away at handoff points, potentially violating compliance or security controls without visible error.
Implement requirement-integrity checks and end-to-end tracing at every sub-agent handoff in production multi-agent systems.
When AI agents delegate tasks to other AI agents, requirements can be silently lost — structured integrity checks are essential before enterprise adoption.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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