AgentBound: Verifiable Behavioral Governance for Autonomous AI Agents

2026-06-29

AI Summary

This work addresses the lack of context-aware, verifiable governance mechanisms in existing AI agents, which hinders dynamic assessment of the legitimacy of authorized actions. The paper proposes AgentBound, a runtime governance framework that leverages a tripartite authority structure—comprising delegated authorization, owner-signed behavioral charters, and site-specific action contracts—to conservatively evaluate each action through a formal decision model, yielding deterministic allow, review, or deny outcomes. It introduces verifiable governance receipts and a continuous delegation model, enabling cryptographic binding of decisions, independent replayable verification, and dynamic permission updates. Evaluation on the AgentBound-Bench benchmark demonstrates the system’s effectiveness in ensuring governance correctness, enforcing compositional authority logic, and supporting accountability, thereby providing AI agents with a deterministically governed, independently verifiable layer of oversight.

Abstract

Autonomous AI agents increasingly perform consequential actions on behalf of human principals, including financial transactions, external communications, and enterprise workflows. Existing agent infrastructure relies on identity federation and delegated authorization to authenticate workloads and control resource access, but it cannot determine whether an authorized action should be executed under the current behavioral and operational context. We present AgentBound, a runtime governance framework that provides verifiable behavioral oversight for autonomous AI agents. AgentBound evaluates each proposed action using three independent authorities: delegated authorization, owner-signed behavioral constitutions, and site action contracts. Their judgments are conservatively composed through a formal decision model to determine whether an action should be permitted, reviewed, or denied before execution. To provide accountability, AgentBound generates cryptographically verifiable governance receipts that bind every action to the exact delegation, policy, and semantic artifacts governing the decision, enabling independent replay verification and policy provenance. The framework also introduces standing delegation for long-running agents, allowing periodic workloads to operate under continuously refreshed governance policies while preserving revocability and bounded authority. We present the formal foundation, system architecture, governance receipt protocol, and AgentBound-Bench, a benchmark framework for evaluating governance correctness, authority composition, and accountability. Rather than replacing model alignment, AgentBound complements it by providing a deterministic governance layer between authorization and execution, transforming governance from a process that must be trusted into one that can be independently verified.

Problem

• autonomous AI agents
• behavioral governance
• verifiable oversight
• runtime governance
• action authorization

Innovation

• verifiable governance
• behavioral constitution
• governance receipts
• standing delegation
• autonomous AI agents