Mosaic AI Agent Framework

A suite of tools by Databricks for building, deploying, and evaluating high-quality AI agents and RAG applications.

4.0 (4)

审阅者 Daniel Nikulshyn·更新 2026年5月

RAG Framework AI Agents Deployment Enterprise Evaluation Databricks LLMOps

概览

Mosaic AI Agent Framework — A suite of tools by Databricks for building, deploying, and evaluating high-quality AI agents and RAG applications.

使用场景

Build Production RAG Applications

Develop retrieval-augmented generation apps that connect LLMs to enterprise data sources for accurate, context-aware responses.

Deploy AI Agents at Scale

Create and deploy AI agents within the Databricks ecosystem, leveraging integrated tooling for reliable production rollout.

Evaluate Agent Quality

Assess the performance and quality of AI agents and RAG pipelines using built-in evaluation tools to ensure high standards.

Iterate on LLM Workflows

Prototype, test, and refine generative AI workflows in a unified framework that streamlines the agent development lifecycle.

评测

4.0

4 个评分的平均值。

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Nadia Petrova

Years in this space

I've evaluated a lot of these over the years. What stands out here is the integrations — handled better than most — and the value for money is strong. The mobile experience lags is my one real gripe. Worth the time if this is your use case.

Kwame Mensah

Solid for our team

We rolled this out across the team last quarter and it saves real time. The core workflow fits neatly into how we already work, and the integrations removed a step we used to do by hand. The mobile experience lags, which is the main caveat, but it has held up under daily use.

Mei-Ling Wong

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on the integrations, and it is genuinely easy to set up caught me off guard. Pricing gets steep at scale is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Tomáš Novák

Solid for our team

We rolled this out across the team last quarter and the value for money is strong. The automation fits neatly into how we already work, and the API removed a step we used to do by hand. The mobile experience lags, which is the main caveat, but it has held up under daily use.