Structured Authority Injection (SAI): protocol for persistent human overrides in LLM agents
The paper proposes Structured Authority Injection (SAI), a protocol that routes operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls and maintains a ranked authority context prepended to each LLM prompt. SAI defines three override classes (Constraint, Priority, Strategy) with distinct persistence and precedence enforced via a directed authority channel, requires no model fine-tuning, and evaluation across nine LLMs and eight scenarios shows reduced ambiguous directive interpretation and mitigated authority decay (with prompt overhead reductions up to ~38% in the strongest models), though benefits vary by model and scenario.