MIDAS: Design and Practice of an Execution Permission Governance Platform for AI Agents

This article explores how the MIDAS platform addresses execution authorization and decision security issues in AI agents and enterprise workflows through a unified permission governance framework. As an open platform solution, MIDAS's core is to explicitly model "execution authority" and overlay it on existing IAM systems, providing mechanisms such as interception evaluation, permission delegation, and audit compliance to help enterprises manage agent permissions in a secure and controllable manner.

Ambiguity of Permission Boundaries

Traditional software system permission models are clear, but AI agents execute tasks across systems and resources, making static permission allocation difficult to adapt to dynamic scenarios.

Diversity of Decision Surfaces

Agents need to act at multiple decision points such as API calls and database operations; scattered permission control points make unified audit management difficult.

Gray Areas in Human-Agent Collaboration

In human-agent collaboration scenarios, permission boundaries are complex, requiring fine-grained delegation mechanisms and revocation capabilities.

Platform-Based Governance

Does not replace existing identity authentication systems; instead, it is overlaid as a governance layer, preserving IAM infrastructure and supporting plugin extensions for new decision surfaces.

Centralized Management of Execution Authority

Models "execution authority" as a first-class citizen; decision requests carry explicit authorization context (initiator, permission basis, conditions, validity period), making explicit authorization more secure and auditable.

Open Standards and Interoperability

Emphasizes standard protocols; agents/workflow systems integrate via standard interfaces to avoid vendor lock-in and promote ecosystem formation.

Interception and Evaluation of Decision Points

Deploy interception points on decision surfaces to trigger evaluation processes: verify legitimacy, check context, assess risks, and conduct manual approval if necessary—shifting from passive verification to active governance.

Permission Delegation and Proxy Chain

Supports fine-grained delegation (scope, time, conditional restrictions); the proxy chain can trace back to the original authorization source.

Audit and Compliance Support

Records the complete context of decisions (request details, evaluation logic, approval process, results) to support post-hoc analysis and compliance reporting.

Automated Workflows

Governs workflow engine operations to ensure actions are within authorized scope; sensitive operations require approval.

Multi-Agent Collaboration

Provides a unified governance view to coordinate permission delegation and resource access, preventing privilege escalation attacks.

Human-Agent Hybrid Decision-Making

Supports human-agent collaboration for high-risk decisions: agents propose solutions, which are executed after human approval; manages the integrity and traceability of the approval chain.

Performance and Latency

Requires an efficient policy evaluation engine, supports caching/asynchronous processing; pre-authorization or batch authorization can be used for latency-sensitive scenarios.

High Availability

As an infrastructure, it needs high availability; trade-offs between safe degradation or operation rejection in case of failures are required.

Policy Expressiveness

Balancing policy expressiveness and manageability is a core challenge in policy engine design.

From Tool to Infrastructure

Represents the evolution of AI governance towards infrastructure; may become a standard component of enterprise IT architecture.

Standardization Trend

Promotes unified standards and best practices in the field of agent permission governance.

Connection with AI Security Research

Permission governance is an important part of AI security; it can connect with AI alignment and interpretability research to build a comprehensive security system.

MIDAS proposes a systematic platform solution to address the challenges of AI agent permission governance. Its core value lies in treating execution permissions as explicit governance objects, providing unified, auditable, and scalable management capabilities—serving as a key guarantee for enterprises to deploy AI agents safely and reliably.