- The paper introduces CAGE-1 as a 12-dimension evaluation framework enabling prebind assurance, ensuring that critical agentic AI actions are verified before execution.
- It details methodological innovations across dimensions such as identity validation, policy enforcement, and auditability, validated via real-world scenarios in finance, HR, and IT.
- The framework provides actionable insights for risk management, enabling enterprises to achieve controlled, auditable, and trustworthy deployment of agentic AI.
Motivation and Context
Enterprise adoption of agentic AI marks a distinct operational shift: AI agents are now expected not only to generate outputs, but to take direct action within business workflows, entailing system modification, data exchange, and obligation creation. This phase introduces qualitatively new risks, as enterprise leaders require comprehensive answers about authority, policy application, evidence origin, memory validity, tool permission, auditability, and the ability to intervene before consequences materialize. The CAGE-1 framework is proposed as a formal response to these demands, moving beyond model-centric evaluation to assurance of governance, operational control, and provable safety in enterprise agent deployments.
CAGE-1 Framework: Scope and Dimensions
CAGE-1 (Control, Assurance, and Governance Evaluation) is a 12-dimension evaluation framework that systematically assesses readiness, trust, and risk management for enterprise-deployed agentic AI. The framework explicitly introduces and centers "Prebind Assurance"—the capacity to prove that all control, policy, evidence, and authority checks pass before any agentic action becomes operationally binding or consequential.
The evaluation dimensions encompass:
- Identity and Authority Validation
- Policy Enforcement
- Retrieval Trust
- Memory Integrity
- Tool Safety
- Planning Control
- Human Oversight
- Audit and Replayability
- Conflict and Boundary Handling
- Failure Behavior
- Operational Readiness
- Business Fitness
Each dimension is scored from Level 0 (Uncontrolled) to Level 4 (Assured). For high-consequence agents, Level 3 (Enforced) or Level 4 (Assured) is considered a deployment prerequisite.
Prebind Assurance: Core Construct
Prebind Assurance establishes a tangible, testable governance boundary: prior to the effectuation of any critical action (payment, access, remediation, data modification), the system must prove—with structured, tamper-resistant evidence—that all required authority, policies, and risk controls are satisfied. If any condition fails, the action is held, narrowed, escalated, refused, quarantined, or rendered "no-bind" (non-effective). Crucially, Prebind Assurance is positioned as a prerequisite for operational trust—auditing after the fact is insufficient if enterprise control over action boundaries cannot be demonstrated and proven before state change.
The framework operationalizes this through architectural requirements: enforcement points for identity/authority validation, policy decisioning, governed retrieval/memory, tool usage brokering, multi-factor approvals, and a dedicated evidence service generating persistent "receipts" and replayable traces.
Illustrative Application Cases
The framework is validated through scenario-driven case studies:
- Finance agent (high-stakes, payment approval): CAGE-1 requires separation between payment proposal and approval, enforces policy thresholds, and mandates that execution remains non-effective absent requisite approval, along with comprehensive evidence capture and replayability.
- HR policy agent (conflicting or stale policy sources): Policy provenance and freshness are enforced; exceptions are escalated, and agent responses are narrowly scoped with provenance receipts.
- IT operations agent (sensitive automation, eg. production restarts): CAGE-1 mandates classification and approval routing for high-impact actions, ensuring blast radius is assessed and action is logged with full audit and replay capacity.
In each case, deployment decisions rest on multidimensional assurance scores and boundary outcome receipts.
Trust Barrier Model
The CAGE-1 trust barrier model identifies five cumulative layers impeding operational adoption: action constraint, evidence completeness, authority proof, accountability attribution, and prebind assurance. The highest-order barrier is the lack of Prebind Assurance, which prevents enterprises from having deterministic custody over the formation of actionable consequences.
Relationship to Existing Frameworks
CAGE-1 is complementary to contemporary AI risk, governance, and regulatory frameworks (NIST AI RMF, ISO/IEC 42001, EU AI Act), but it distinguishes itself through its explicit agent-centric design, its focus on action-boundary interception, and its insistence on pre-action evidence, replay, and remediation capabilities. Unlike post-hoc compliance, CAGE-1 is intended as a practical, runtime-enforceable assurance model, creating artifacts such as agent system cards, authority matrices, policy control maps, memory governance records, tool execution registers, and prebind assurance receipts for operational use.
Implications for Research and Enterprise AI Practice
Theoretical Implications
CAGE-1 reifies the distinction between capability and trust in the design of agentic systems: successful task completion is decoupled from (and subordinate to) controlled, auditable, and governable execution. Prebind Assurance establishes a new evaluable proof discipline—similar in spirit to formal verification in safety-critical software—where provenance, authority, and compliance conditions must be proven before state-altering execution is permitted.
Practical Implications
For enterprise environments, CAGE-1 provides a scalable evaluation protocol that surfaces and quantifies systemic weaknesses in control planes, policy mapping, and tool permissioning. It requires organizations to formalize operational boundaries, systematize memory and retrieval governance, and implement technical runtime enforcement and evidence capture. Enterprises adopting this framework can achieve risk-adjusted, stepwise deployment of agentic AI—beginning with low-autonomy, human-in-the-loop agents and advancing to fully trusted, assured, auditable automation.
Future Developments
Deployment of agentic AI at enterprise scale will increasingly depend on the adoption or evolution of frameworks like CAGE-1. Future research avenues may include:
- Formalization of Prebind Assurance as a functional requirement in regulatory compliance and AI safety standards.
- Automated generation and testing of CAGE-1 artifacts within MLOps and AI platform toolchains.
- Extension of boundary proof concepts to inter-agent systems, federated architectures, and cross-enterprise workflows.
- Integration of CAGE-1 scoring into continuous monitoring, anomaly detection, and incident response systems.
- Comparative studies quantifying residual risk reduction and operational efficiency gains under CAGE-1 versus legacy compliance models.
Conclusion
CAGE-1 constitutes a comprehensive, operationally targeted evaluation framework bridging the gap between AI capability and enterprise trust. By centering Prebind Assurance and requiring formal, evidence-backed enforcement of policy, authority, provenance, and auditability before enterprise actions become effective, the framework provides a rigorous and scalable pathway for the deployment of agentic AI in high-stakes environments. Its adoption represents a material advance in enterprise AI governance, conditioning future research and platform evolution toward provable governance and controlled autonomy.