microsoft-foundry

You can streamline AI agent deployment pipelines by configuring resource allocation, model versioning, and evaluation to ensure scalable, compliant AI solutions. This Skill automates those steps, including iterative improvement for enterprise operations.

The best way to deploy and evaluate Foundry AI agents is to provide a project endpoint, configure models and policies, and automate evaluation runs. This approach compares agent versions and optimizes prompts based on performance over datasets.

Azure AI resource management supports automated quota requests, permissions setup, and pipeline configuration for scalable AI operations. This allows organizations to deploy multiple models while maintaining compliance and resource limits.

To optimize prompts and compare model versions, you automate evaluation runs during AI deployment. This process evaluates performance over various datasets, enabling iterative improvement of assistant capabilities with minimal manual effort.

You configure custom prompts or container images for AI agents when deploying prompt or hosted agents for complex workflows. This ensures the deployed agent handles specific enterprise operational tasks and policies effectively.