Design Patterns for Building Reliable AI Agent Workflows in Enterprise Applications

Hi Microsoft community,

AI agents are becoming increasingly common in enterprise applications, but designing reliable workflows around them introduces new challenges.

A common architecture pattern is separating the AI reasoning layer from the execution layer, where agents can make decisions while business logic, permissions, and validations remain within controlled services.

I would like to understand how teams are approaching:

- Designing scalable AI agent architectures

- Connecting agents with enterprise APIs and services

- Managing agent state and workflow execution

- Handling failures, retries, and human approval steps

- Monitoring and evaluating agent performance in production

What architecture patterns and Microsoft technologies are you finding effective when building production-ready AI agent solutions?

Looking forward to learning from the community.