The Azure AI Agent Service allows you to build semi-autonomous software that can be given a goal and will work to achieve that goal without you knowing in advance exactly how it's going to do that or what steps it's going to take. This is a powerful tool for automating tasks and processes that are too complex to be easily automated with traditional software.
In our experience talking to hundreds of organizations, we have learned that developing secure, reliable agents rapidly requires four primary ingredients:
- Rapidly develop and automate processes: Agents need to seamlessly integrate with the right tools, systems and APIs to perform deterministic or non-deterministic actions.
- Integrate with extensive memory and knowledge connectors: Agents need to manage conversation state and connect with internal and external knowledge sources to have the right context to complete a process.
- Flexible model choice: Agents built with the appropriate model for its task can enable better integration of information from multiple data types, yield better results for task-specific scenarios, and improve cost efficiencies in scaled agent deployments.
- Built-in enterprise readiness: Agents need to be able to support an organization's unique data privacy and compliance needs, scale with an organization's needs, and complete tasks reliably and with high quality.
- Understand the Azure AI Agent Service
- Understand Azure OpenAI Chat Completion Function Calling
- Learn how to build conversational agents
- Azure AI Agent Service
- Jupyter Notebooks
- OpenAI SDK
| Resources | Links | Description |
|---|---|---|
| Future Learning 1 | Getting started with Azure AI Agent Service | Learn more about the Azure AI Agent Service |
| Future Learning 2 | Azure AI Agent Service API Documentation | Learn more about Azure AI Agent Service APIs |
 Dave Glover 📢 |
 Chris Noring 📢 |
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments.
Many of these resources can be found at:
Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms.
Please consult the:
to be informed about risks and limitations.
The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior.
provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful.
We also have an interactive Content Safety Studio that allows you to view, explore and try out sample code for detecting harmful content across different modalities.
The following:
guides you through making requests to the service.
Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using
You can evaluate your AI application in your development environment using the
Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice.
To get started with the prompt flow sdk to evaluate your system, you can follow the:
Once you execute an evaluation run, you can: