Key takeaways: Toward and agentic AI governance framework

Effective governance of AI agents requires a comprehensive approach
that addresses their unique characteristics while building on established
governance principles. We recommend a framework with five key components:

1. Establish continuous AI discovery and oversight
You can't govern what you can't see. The foundation of effective agent governance begins with visibility. Build a comprehensive AI inventory that captures:

Custom AI use cases deployed across your organization

AI capabilities embedded within commercial tools and platforms

Experimental and prototype agents that may evolve into production systems

Implement discovery mechanisms that track AI agents from ideation through deployment and ensure governance keeps pace with innovation rather than trying to catch up retroactively.2. Secure agents with proper data control
Every agent has input and output data flows
that require governance. Implement data governance controls specifically designed
for agent operations, including:

Establish access controls that apply the
principle of least privilege

Define clear data policies that account
for automated processing

Create safeguards around sensitive data categories (HR, legal, customer PII)

Ensure the data that agents consume and produce complies with both internal standards and external regulatory requirements across all jurisdictions where your organization operates.3. Ensure human oversight where needed
Autonomy doesn't mean abdication of responsibility. You can design meaningful
human oversight into agent operations by:

Implementing risk-based review checkpoints that scale with potential impact

Establishing override protocols that allow intervention when necessary

Creating clear accountability chains that connect agent actions to responsible individuals

Focus human attention where it adds the most value and mitigates the most significant risks without creating unnecessary bottlenecks or performative reviews.

Align AI agents with regulatory requirements
Regulations are evolving rapidly—preparation begins now. Build compliance into agent design rather than retrofitting it later:

Document key decisions, data sources, and risk assessments throughout the development process

Monitor emerging regulations across global markets to anticipate requirements

Design governance frameworks flexible enough to adapt as regulatory landscapes evolve

Focus on comprehensive documentation that will demonstrate responsible development regardless of which specific regulations ultimately apply to your deployments.Build explainable, traceable
and transparent systems
Trust requires transparency—for users, stakeholders and regulators alike. That means, your organization needs to create agent architectures with explainability as a core design principle:

Implement logging that captures not just actions but decision factors

Design transparent processes that demonstrate how agents reach conclusions

Develop traceability mechanisms that connect outcomes to inputs

Remember: explainability isn't just a compliance requirement—it's essential for building stakeholder trust and enabling continuous improvement of agent capabilities.