Structured Authority Injection Enables Persistent Human Control for LLM Agents
Researchers propose Structured Authority Injection (SAI), a protocol that routes human corrections through schema‑validated MCP calls and preserves authority across planning cycles.
## New protocol for human‑in‑the‑loop control A recent arXiv submission (arXiv:2610.22373v1) addresses a critical gap in large‑language‑model (LLM) agents used as autonomous orchestrators. While these agents excel at coordinating distributed tasks, existing systems lack a protocol‑level method for operators to inject binding directives that survive multiple planning cycles.
## Structured Authority Injection (SAI) The authors introduce Structured Authority Injection (SAI), which channels operator corrections through typed, schema‑validated Model Context Protocol (MCP) tool calls. Each LLM prompt is prefixed with a ranked authority context, ensuring that human instructions retain influence despite accumulating context.
## Override classes and persistence SAI defines three distinct override classes—Constraint, Priority, and Strategy—each with its own persistence semantics. An explicit precedence order is enforced, allowing operators to specify how long a directive should remain effective and how it should interact with other instructions.
## Implications for multi‑agent systems By providing a clear, validated pathway for human directives, SAI aims to reduce ambiguity inherent in free‑text overrides and prevent authority erosion. The protocol could become a foundational component for safer, more controllable LLM‑driven multi‑agent environments.
*Source: arXiv cs.AI, 2026‑10‑19*
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