Three tips for building agentic AI systems on cloud platforms

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.

It’s much better to loosen restrictions later than to fix expensive or dangerous behaviors. Always pair every agentic AI deployment with detailed cloud-based controls such as least-privilege access, explicit approval gates for risky actions, rate limits, and comprehensive audit logs.

Choose cloud-native integrations for fast context and action

Agentic AI requires two key elements to be truly effective: access to quick, accurate context and the ability to act through reliable interfaces. A common mistake is to treat the AI system as a standalone component rather than developing it as a first-rate entity that can utilize the cloud platform’s native integrations.

“How do I get started?” is the question, and “cloud-native service composition” is the answer. Rely on platform capabilities like AWS EventBridge or Azure Event Grid to feed live context to your agents. Connect your agents to service catalogs, security, and orchestration through built-in SDKs instead of fragile custom interfaces. Managed workflow services like AWS Step Functions and Azure Logic Apps can be used to sequence complex actions and to handle state.

For example, an omnichannel retailer builds a pricing optimization agent in the cloud. Initially, they write hand-crafted integrations to link the AI to pricing databases, inventory, and notification endpoints. The result is a complex, brittle system where minor API changes break key operations. When the team switches to cloud-native connectors and serverless orchestration, they not only cut the maintenance workload in half, but they also improve reliability and recovery speed.

The takeaway here is clear: Design your agentic AI to be a natural part of your cloud ecosystem. Do not waste time on plumbing or “glue code.” Use managed services for data, event management, and orchestration wherever possible so you can focus on what makes your agent intelligent. This strategy also future-proofs your work since cloud services evolve faster than you can maintain hand-built connectors.

Optimize feedback loops and continuous learning

What truly distinguishes agentic AI from traditional automation is its capacity for continuous learning. In cloud environments, feedback loops are not only vital for the underlying model, they are essential for aligning agentic behavior with business goals and for ensuring long-term resilience.

From day one, agentic AI architects itself for robust telemetry and feedback. Cloud services such as CloudWatch, Azure Monitor, and GCP Cloud Logging let you instrument your agents’ actions and capture their outcomes in detail. Feed this data into machine learning pipelines to retrain your agents and establish monitoring and alerting dashboards so both humans and systems can easily identify drift or emergent misbehavior.

Imagine a financial services firm that deploys agent-based document processing on Azure. By capturing every action and feeding failure cases into retraining routines, they manage to reduce their exception rates by 50% within six months. What’s more, by tying results back to workflow changes, they gain the trust of compliance and audit teams who can now see—step by step—how the system improves.

Remember, agentic AI is never a “set it and forget it” proposition. Use your cloud platform’s monitoring and retraining features to ensure agents evolve and stay aligned with the needs of the business. Continuous measurement, adjustment, and improvement should become fundamental habits, not afterthoughts.

Agentic AI on cloud platforms succeeds when organizations leverage the cloud’s strengths: enforceable guardrails, high-velocity integration, and the tools to learn and evolve. The most successful teams build thoughtfully, focusing on practical safety and efficiency as much as autonomy. Ultimately, agentic AI should become a trusted business partner—not a source of unexpected crises.