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. Databricks launched Mosaic Agent Bricks as a solution to that challenge. 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. Agent Bricks automates the entire optimization pipeline. The platform takes a high-level task description and enterprise data. It handles the rest automatically. The platform offers four agent configurations: Information Extraction; Knowledge Assistant; Custom LLM; Multi-Agent Supervisor. Databricks also announced the general availability of its Lakeflow data engineering platform. Lakeflow solves the data preparation challenge. It unifies three critical data engineering journeys that previously required separate tools. Ingestion handles getting both structured and unstructured data into Databricks. Transformation provides efficient data cleaning, reshaping and preparation.