Abstract

This note addresses the operational risks of implicit authority in autonomous systems. It proposes an authority-first architecture that treats agents as proposers rather than actors. By decoupling permission logic from reasoning loops, engineers can scale agentic systems without risking unauthorized state changes.

Context & Motivation

Current agent development often conflates reasoning, planning, and execution. As model capabilities increase, the primary constraint shifts from capability ("what can it do") to authority ("what is it allowed to do"). System prompts and implicit tool permissions are insufficient for production safety. Robust system integrity requires a formal separation of authority from the reasoning loop.

Core Thesis

Agents are proposers, not actors. Authority must be explicit, declarative, and external to the reasoning loop. Implicit authority is a failure mode; explicit authority is a governance requirement.

Mechanism & Model

The authority-first model strictly decouples the agent's reasoning process from the system's permission logic.

The Reasoning-Authority Gap
In a standard loop, an agent decides to act and executes that action. In an authority-first architecture, the agent generates a proposal. This proposal is intercepted by an external authority system that evaluates it against a static specification before execution.

The Five Primitives
Every authority decision is reduced to five decidable components:

  • Principal: The identity of the agent or entity attempting the action.
  • Action: The specific operation being attempted (e.g., DeleteObject, SendEmail).
  • Resource: The target object or system affected by the action.
  • Context: Situational data (e.g., time of day, network origin, system state).
  • Decision: A binary Allow or Deny, or a transition to Escalate.

The Authority Specification
The specification is a declarative, finite map of the system's permission surface. It is external to the agent, stable under learning, and cannot be modified by the agent's reasoning process.

Concrete Examples

Infrastructure Management
An agent tasked with cost optimization identifies an unused storage bucket.

  • Proposal: Agent proposes DeleteBucket on prod-backups.
  • Evaluation: The authority system checks the policy against the resource tags.
  • Result: Denied. The policy forbids DeleteBucket on resources tagged protected, regardless of the agent's reasoning regarding cost.

Customer Support
An agent handles a refund request.

  • Proposal: Agent proposes IssueRefund for $500.
  • Evaluation: The authority system checks the Principal (Agent) against the Action (Refund) and Context (Amount > $200).
  • Result: Escalate. The system requires human approval for refunds exceeding the defined threshold.

Trade-offs & Failure Modes

  • Latency: Externalizing authority checks introduces performance overhead.
  • Specification Rigidity: Narrow specifications may cause "policy-induced paralysis," where the agent cannot fulfill objectives despite having the reasoning capacity.
  • Maintenance: Keeping the authority specification synchronized with evolving toolsets requires engineering overhead.
  • Scope Limitation: This architecture does not correct "bad reasoning"; it only prevents bad reasoning from translating into unauthorized actions.

Practical Takeaways

  1. Invert the Mental Model: Design the system such that the agent has zero inherent power. Every tool call is a request, not a command.
  2. Externalize Policy: Use a dedicated policy engine (e.g., Cedar) to manage permissions. Do not embed rules in the system prompt.
  3. Map the Surface: For every tool provided to an agent, explicitly define the Principal, Action, and Resource primitives.
  4. Fail Closed: If the authority system cannot reach a decidable Allow, the default state must be Deny.

Positioning Note

  • Not Academic Research: Focuses on implementation patterns and operational safety rather than formal proofs.
  • Not Blog Opinion: Prioritizes durable architectural constraints over transient tool preferences.
  • Not Vendor Documentation: Provides a conceptual framework applicable across different stacks.

Status & Scope Disclaimer

This is exploratory lab work from rmax lab. It represents a validated approach to agent safety but is not an authoritative standard. Models are subject to refinement as agentic engineering evolves.

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