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

4.0 (4)

Reviewed by Daniel Nikulshyn·Updated June 2026

AI Agents RAG Enterprise Databricks LLMOps Evaluation Deployment Framework

Overview

The Databricks Agent Framework (AAF) is a set of tools provided by Databricks for building, deploying, and governing AI agents. These agents can connect to any tool, database, or SaaS application through a single, governed protocol (Model Context Protocol, MCP). The framework includes integrated AI development, experimentation, and deployment into serverless computing using Databricks Apps without managing infrastructure. Agent Bricks and Unity Catalog are essential for governance and security; they apply predefined policies on models, tools, and connections while enforcing rate limits, fallbacks, and other safeguards required for prompt injection prevention, sensitive data detection, and content filtering.

Key features

Agent Bricks
Unity Catalog
Databricks Apps
Model Context Protocol (MCP)
Centralized governance
Per-user control

Pricing

Model: Contact for pricing
Category: AI Agent Development Frameworks
Rating: 4.0 / 5 (4)

Use cases

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.

Pros & Cons

Pros

Centralized governance
Per-user control
Serverless computing

Cons

Limited non-AI integration
No pre-built agents
Documentation gap

Battle record

Across 1 battle in the Pantheon.

1st

2nd

3rd

Last battle

#6
AI Agent Development Frameworks Showdown — July 19, 2024
Jul 19, 2024 · #6 of 7

Reviews

4.0

Average from 4 ratings.

Nadia Petrova

Apr 3, 2026

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

Dec 5, 2025

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

Oct 16, 2025

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

Jul 11, 2025

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.

Q&A

Is it possible to switch between different large language models without re‑architecting my agents?

Yes. Agent Bricks lets you access models from OpenAI, Anthropic, Google, Meta, and open‑source providers through a single contract, and you can swap models instantly to optimize cost, quality, or performance without redesigning your stack.

Asked by Fernando Rojas · Feb 5, 2026

What deployment options are available and do I need to manage infrastructure?

Agents are deployed serverless via Databricks Apps, so you can push code from any IDE or no‑code builder directly to managed compute without provisioning or maintaining servers.

Asked by Carlos Mendoza · Jan 4, 2026

Can I integrate the framework with any SaaS or database, and what protocol is used?

Agents connect to any tool, database, or SaaS application through the Model Context Protocol (MCP), a single governed protocol that standardizes access while applying the platform’s security policies.

Asked by Vincenzo Greco · Dec 12, 2025

How does the Mosaic AI Agent Framework handle governance and security for model and tool access?

Governance is built into the platform via Agent Bricks and Unity Catalog, which apply predefined policies, enforce rate limits, and add safeguards like prompt‑injection prevention, sensitive data detection, and content filtering. All agents, models, and tool connections are tracked with ownership and permissions in a single system of record.

Asked by Sanjay Gupta · Dec 7, 2025