1. Establish Governance Before Giving AI Agents Access
Agentic AI systems can access data, interact with tools and take actions across workflows. Before deployment, establish clear policies for security, compliance and data handling. Limit agents to the minimum privileges required, restrict access to sensitive information and monitor interactions with external systems. Strong governance helps reduce the risk of data exposure, compliance violations and unintended actions.
2. Evaluate Costs and Expected Outcomes
Agentic AI can accelerate application modernization and other initiatives, but organizations should carefully assess costs and expected benefits before scaling deployments. Pricing models are often tied to usage, and human oversight remains necessary for quality and security. Define business objectives and success metrics early to determine whether an agentic AI project is delivering measurable value.
3. Build Human Oversight Into Quality Assurance
AI-generated outputs should be reviewed and tested before they are put into production. Require human approval for high-impact actions such as code changes, deployments or workflow modifications. Monitor agent activity, establish alerts for unusual behavior and limit agents to trusted tools and environments. Combining automation with human oversight helps improve reliability while reducing operational risk.
UP NEXT: Agencies must learn how to securely incorporate AI agents.
