As LLM-based agents transition into roles as autonomous orchestrators within distributed systems, a significant control gap has emerged: the absence of a protocol-level mechanism for human operators to inject binding directives that persist across multiple planning cycles.
The Challenge of Authority Erosion
Research published on ArXiv highlights that traditional free-text overrides often fail due to ambiguous interpretation and "authority erosion." This occurs as operator instructions lose their influence within the expanding context of an agent's planning iterations. Without schema validation, the agent may misinterpret or eventually ignore critical human constraints.
Introducing Structured Authority Injection (SAI)
The proposed Structured Authority Injection (SAI) protocol addresses these vulnerabilities by routing operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls. SAI categorizes overrides into three distinct classes, each with specific persistence semantics:
- Constraint: Binding limitations on agent actions.
- Priority: Directed focus on specific objectives.
- Strategy: High-level guidance on methodology.
The method maintains a ranked authority context prepended to every prompt, enforced through a channel that remains inaccessible to the agent's internal planning process. Notably, SAI requires no model fine-tuning or access to model weights, making it applicable to any instruction-tuned LLM that supports MCP.
Performance and Efficiency
Evaluations across nine LLMs and eight failure scenarios demonstrated that SAI effectively mitigates authority decay. While the benefits vary by architecture, the strongest models showed a reduction in prompt overhead of up to 38%. This suggests that SAI provides a viable design pattern for systems requiring rigorous human-in-the-loop oversight.
