Intelligent and speedily maturing Agentic AI systems can now autonomously plan, act, and execute multi-step tasks. Powered by large language models (LLMs), these systems are ready to be the extended workforce that businesses need to automate triaging, decision-making, and other operations. However, to ensure higher productivity, reduced manual workflows, faster response, and auto-managed processes, this workforce too needs an “HR”. These will be “habits and rules” that the organizations will need to implement for Agentic AI governance.  

The benefits of Agentic AI come with complex new risks, including sensitive data exposure, automated fraud, and hijacked agent workflows. This leaves very few businesses actually ready to proactively govern agentic AI. Therefore, just as HR defines protocols, permissions, and escalation paths for the human workforce, Agentic AI governance must do the same for AI agents. 

How Majors Are Testing Agentic AI 

Global bodies, led by entities like the AI Safety Institutes (AISI), are actively designing evaluation methodologies tailored to agentic AI. These are specifically built around risks like information leakage, cybersecurity, and fraud. Aimed at piloting novel evaluation approaches and refining best practices, these methodologies are crucial for encouraging Agentic AI in multinational coordination, prompt design, and benchmark interoperability. Here are some recent efforts done for this purpose: 

• Collaborative Testing Across Countries: A third joint exercise (July 2025) involving the UK, Singapore, Canada, EU partners, and more aligned global approaches to two priority risk strands: information leakage & fraud (led by Singapore), and cybersecurity threats (led by UK AISI). 
• Methodological Rigor: Teams documented choices around prompt temperature, parsing limits, multilingual prompts, model types (open vs. closed weights), and evaluation platforms (e.g., Moonshot vs. Inspect), revealing that small methodological decisions significantly influence results. 
• Threat Modeling Frameworks: New frameworks such as MAESTRO (developed by CSA), which layer agent security analysis across architecture layers (environment, tools, goals), extend STRIDE, PASTA, and LINDDUN into the agentic domain, emphasizing dynamic risk, inter-agent interactions, and continuous monitoring.

Building Agentic AI Governance 

Based on these evaluations, businesses can begin constructing governance frameworks that strike a balance between innovation and control. This will require the governance strategies to be translated for their operational contexts. Think of agentic systems like new hires who you can’t let access critical systems without proper oversight and security measures in place. Agentic AI systems, therefore, need defined roles, risk controls, and performance feedback loops. 

• Define explicit evaluation protocols: Organizations should standardize their testing inputs across languages and multilingual contexts. This will help ensure that the agents are evaluated under diverse, real-world conditions. To maintain consistency and enable comparative insights, businesses should also rely on dual-evaluator setups that can cross-validate outcomes across platforms. 
• Adversarial testing and red teaming: To truly understand agentic vulnerabilities, companies must simulate real-world threats using red teaming techniques. This includes running hijacking scenarios to expose weak points. Tests should also incorporate edge cases such as fraudulent requests, prompt injections, and subtle manipulations that might bypass conventional filters. 
• Threat modeling via agent-focused frameworks: Agentic systems require layered threat models that capture their complex operational environments. Special focus should be placed on threats where autonomy intersects with external tool execution, such as unauthorized API calls or dynamic code execution. 
• Continuous monitoring and feedback loops: Companies should implement anomaly detection to flag deviations in agent behavior and to maintain detailed logs of agent actions. This will also help with decisions for auditability and periodic re-evaluation of agents using updated benchmarks and simulated attack scenarios. 
• Governance culture and readiness: Finally, successful governance relies as much on people and process as it does on technology. Agents should be treated like digital employees, with clearly defined identities, permissions, and accountability pathways. Teams must be trained in monitoring agent behavior, escalating incidents, and ensuring ethical deployment. 

The Next Competitive Advantage 

Agentic AI is a structural redefinition of how digital work will be done in the times of accountable yet autonomous AI. The organizations that will lead in this next era are those that recognize agents as operational actors, not just software. That means building governance systems that act like digital HR to assign roles, conduct evaluations, and report behavioral deviations. The future belongs to those who don’t just build intelligent systems, but who govern them as seriously as they govern people.