By their very nature, agentic AI systems operate with a large degree of autonomy. This autonomy has real value: Cloud-based agents can remediate incidents, optimize costs, or interact dynamically with users. However, when autonomy is unchecked or poorly defined, you often end up with unpredictable behaviors, inefficiency, or even compliance breaches. Let’s look at three ways enterprises can get more business value out of agentic AI.
Keep systems on a tight leash
A practical approach is to start by designing clear, policy-driven constraints for the specific actions that agents can take and under what circumstances. All three leading clouds—AWS, Azure, and Google Cloud Platform—offer tools such as identity and access management (IAM), resource tagging, and policy engines that let you restrict an agent’s privileges and the scope of its actions.
Here’s a quick example: A major SaaS provider launches an AI agent that automatically provisions new compute resources during demand spikes. Within days, the agent’s unchecked autonomy causes large, unexpected cloud costs due to misinterpreted telemetry data. The company responds by creating more restrictive IAM roles in AWS, using tagging to control the agent’s environment, and activating budget alerts and approval workflows for high-impact actions.
