- Databricks announced the public preview of Mosaic AI Agent Framework and Agent Evaluation at the Data + AI Summit 2024.
- These tools are designed to aid developers in creating and deploying high-quality Agentic and Retrieval Augmented Generation (RAG) applications on the Databricks Data Intelligence Platform.
- Challenges in generative AI application development include selecting appropriate quality metrics, collecting feedback, diagnosing issues, and iterating efficiently.
- Mosaic AI Agent Framework and Agent Evaluation offer human feedback integration, comprehensive evaluation metrics, an integrated development workflow with MLflow, and lifecycle management for applications.
- The framework has been successfully used by companies like Corning, Lippert, and FordDirect to enhance AI solutions.
- Pricing for Agent Evaluation is based on judge requests, while Mosaic AI Model Serving follows its own pricing structure.
- Resources available include documentation, demo notebooks, and the Generative AI Cookbook.
Main AI News:
At the Data + AI Summit 2024, Databricks announced the public preview of its Mosaic AI Agent Framework and Agent Evaluation tools. These cutting-edge resources are designed to support developers in creating and deploying high-quality Agentic and Retrieval Augmented Generation (RAG) applications on the Databricks Data Intelligence Platform.
Challenges in Developing Advanced Generative AI Applications
While developing a proof of concept for generative AI applications is relatively simple, creating a high-quality, customer-ready application requires significant effort. Developers frequently face challenges such as:
- Selecting appropriate metrics for assessing application quality.
- Effectively gathering human feedback to evaluate performance.
- Diagnosing the root causes of quality issues.
- Rapidly iterating to enhance application quality before final deployment.
Features of the Mosaic AI Agent Framework and Agent Evaluation
The Mosaic AI Agent Framework and Agent Evaluation offer solutions to these challenges with the following features:
- Human Feedback Integration: Agent Evaluation enables developers to solicit feedback from subject matter experts within their organization, even if they are not Databricks users. This process provides diverse perspectives to refine generative AI applications.
- Detailed Evaluation Metrics: Developed in collaboration with Mosaic Research, Agent Evaluation includes a range of metrics such as accuracy, hallucination, harmfulness, and helpfulness. Responses and feedback are logged automatically, aiding quick analysis and identifying quality issues. AI judges, calibrated with expert input, evaluate responses to pinpoint issues.
- Comprehensive Development Workflow: Integrated with MLflow, the Agent Framework allows developers to log and assess generative AI applications using MLflow APIs. This integration ensures smooth transitions from development to production with ongoing feedback loops to improve application quality.
- Application Lifecycle Management: The Agent Framework offers a streamlined SDK for managing the entire lifecycle of agentic applications, from permissions to deployment with Mosaic AI Model Serving, ensuring scalability and high quality throughout.
Building Effective RAG Agents
Databricks showcased the Mosaic AI Agent Framework’s capabilities by demonstrating the development of a high-quality RAG application. This example involved creating an application that retrieves and summarizes relevant information from a pre-existing vector index, with integration through MLflow to trace and deploy the application. This illustrates how developers can efficiently build, evaluate, and enhance generative AI applications using Mosaic AI tools.
Success Stories and Future Directions
Numerous companies have successfully utilized the Mosaic AI Agent Framework to enhance their generative AI solutions. For example, Corning developed an AI research assistant that indexes extensive documents, improving retrieval speed and response accuracy. Lippert employed the framework to ensure data accuracy in their generative AI applications. FordDirect integrated the framework to develop a unified chatbot for dealerships, improving performance and customer engagement.
Pricing and Further Information
Agent Evaluation pricing is based on judge requests, while Mosaic AI Model Serving follows its own pricing model. Databricks invites customers to explore the Mosaic AI Agent Framework and Agent Evaluation through various resources, including documentation, demo notebooks, and the Generative AI Cookbook, which offer guidance on developing production-quality generative AI applications from initial concept to deployment.
Conclusion:
The introduction of Databricks’ Mosaic AI Agent Framework and Agent Evaluation tools marks a significant advancement in the development of generative AI applications. By addressing key challenges such as quality metrics, feedback integration, and lifecycle management, these tools provide developers with robust support to enhance application performance and reliability. This development is likely to accelerate the adoption of high-quality generative AI solutions across industries, offering a competitive edge to organizations leveraging these advanced tools. The emphasis on comprehensive evaluation and seamless integration with existing workflows underscores Databricks’ commitment to driving innovation and efficiency in the AI space.
