Evaluating AI Agents: A Comprehensive Guide from Microsoft Principal AI Advocate

Join Cedric Vidal, Principal AI Advocate at Microsoft, as he explores the art of evaluating AI agents and demystifies the process with practical demonstrations and real-world examples.

1. Cedric Vidal, Principal AI Advocate at Microsoft, will discuss evaluating AI agents and their safety.
2. Red teaming, presented prior to this session, involves creating data that tests an AI's ability to handle bad situations and generate appropriate responses.
3. The focus of this session is traditional evaluation methods for AI agents when working with a dataset.
4. Ensuring AI agent safety is crucial as they become more independent and powerful, potentially causing chaos or harm.
5. Evaluation should begin early in the AI development process, as soon as a model is chosen.
6. Four layers of evaluation are identified: platform-specific protections, system message and grounding, user experience and financial model, and layering smart mitigations at the application layer.
7. Manual model evaluation is essential for understanding how different models respond to specific prompts.
8. Automatic metrics can sometimes miss important details in model evaluations.
9. AI Toolkit, a new VS Code plugin, allows for easy side-by-side comparison of model responses and was released at Microsoft Build.
10. After choosing a model, the next step is to evaluate it end-to-end as part of an entire system.
11. The AI Toolkit extension in VS Code can be used to build and evaluate AI agents quickly.
12. An example is provided of creating an agent to extract agenda and event information from web pages, using GPT41 as the model.
13. After building a customized AI agent, it's important to evaluate its performance on multiple inputs using a dataset.
14. The evaluation tab in AI Toolkit allows for running an AI agent on a dataset and viewing responses.
15. Customizing Azure Foundry's built-in evaluators can help automate and scale AI agent evaluations.
16. Azure Foundry provides various AI-assisted quality checks, classic NLP metrics, and risk and safety evaluators.
17. After spot checking an AI agent, it is crucial to conduct more thorough checks across a wider range of inputs and automate the evaluation process.
18. Automated evaluation can be done through the Azure Foundry portal or via code.
19. A Python notebook demonstrates programmatically connecting to an Azure AI Foundry project and running evaluations.
20. Quality evaluators, such as relevance, coherence, groundedness, fluency, and similarity, can be used in automated evaluations.
21. Thresholds for evaluation scores can be configured based on the specific application, such as gaming or children's content.
22. Azure Foundry also supports multimodal model evaluations that mix text and images.
23. Evaluating multimodal models is essential for handling multi-turn conversations and generating appropriate responses to complex inputs.
24. Contact information was provided for further questions, with resources available on GitHub, Azure Foundry discussions, and the Azure Foundry Discord server.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!