Databricks Announced the Public Preview of Mosaic AI Agent Framework and Agent Evaluation
Challenges in Building High-Quality Generative AI Applications
Developing high-quality generative AI applications that meet customer standards is time-consuming and challenging. Developers often struggle with choosing the right metrics, collecting human feedback, and identifying quality issues.
Introducing Mosaic AI Agent Framework and Agent Evaluation
These tools address challenges by integrating human feedback, offering comprehensive evaluation metrics, supporting end-to-end development workflow, and providing app lifecycle management.
Building a High-Quality RAG Agent
The Mosaic AI Agent Framework simplifies the process of creating a high-quality Retrieval Augmented Generation (RAG) application. It enables developers to connect to a vector search index, set the index into a LangChain retriever, and deploy the application using MLflow.
Real-World Applications and Testimonials
Companies like Corning, Lippert, and FordDirect have successfully utilized the Mosaic AI Agent Framework to enhance their generative AI solutions, improving retrieval speed, response quality, data accuracy, and customer engagement.
Pricing and Next Steps
Agent Evaluation pricing is based on judge requests, while Mosaic AI Model Serving is priced according to Mosaic AI Model Serving rates. Customers are encouraged to try the Mosaic AI Agent Framework and Agent Evaluation through various resources provided by Databricks.
In conclusion, Databricks’ Mosaic AI Agent Framework and Agent Evaluation empower developers to efficiently build, evaluate, and deploy high-quality generative AI applications, representing a significant advancement in generative AI.
