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Many enterprise AI agent development efforts never make it to production and it’s not because the technology isn’t ready. The problem, according to Databricks, is that companies are still relying on manual evaluations with a process that’s slow, inconsistent and difficult to scale.
Today at the Data + AI Summit, Databricks launched Mosaic Agent Bricks as a solution to that challenge. The technology builds on and extends the Mosaic AI Agent Framework the company announced in 2024. Simply put, it’s no longer good enough to just be able to build AI agents in order to have real-world impact.
The Mosaic Agent Bricks platform automates agent optimization using a series of research-backed innovations. Among the key innovations is the integration of TAO (Test-time Adaptive Optimization), which provides a novel approach to AI tuning without the need for labeled data. Mosaic Agent Bricks also generates domain-specific synthetic data, creates task-aware benchmarks and optimizes quality-to-cost balance without manual intervention.
Fundamentally the goal of the new platform is to solve an issue that Databricks users had with existing AI agent development efforts.
“They were flying blind, they had no way to evaluate these agents,” Hanlin Tang, Databricks’ Chief Technology Officer of Neural Networks, told VentureBeat. “Most of them were relying on a kind of manual, manual vibe tracking to see if the agent sounds good enough, but this doesn’t give them the confidence to go into production.”
From research innovation to enterprise AI production scale
Tang was previously the co-founder and CTO of Mosaic, which was acquired by Databricks in 2023 for $1.3 billion.
At Mosaic, much of the research innovation didn’t necessarily have an immediate enterprise impact. That all changed after the acquisition.
“The big light bulb moment for me was when we first launched our product on Databricks, and instantly, overnight, we had, like thousands of enterprise customers using it,” Tang said.
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