Databricks announced an enhancement to its evaluation framework for AI agents, introducing three new capabilities within its MLflow-powered environment. These features Tunable Judges, Agent-as-a-Judge and Judge Builder are designed to help organizations build, monitor and continuously improve high-quality AI agents at scale.
Key Capabilities
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Tunable Judges enable systematic alignment of evaluation logic with domain-expert standards, simplifying the creation of custom evaluation criteria.
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Agent-as-a-Judge allows the evaluation framework to automatically determine which parts of an agent’s trace should be assessed, removing the need for manual trace-filtering logic.
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Judge Builder provides a visual workflow that brings together domain experts and developers in the review, alignment and lifecycle management of judge models.
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Context & Need
As enterprises deploy AI agents into production with wider user bases and more critical outcomes there is an increasing need to evaluate these agents beyond generic quality metrics. Many real-world use cases require nuanced, domain-specific evaluation aligned with business rules, regulatory standards and operational criteria. Traditionally, building such custom evaluation logic has been time-consuming and required close collaboration between developers and domain experts, creating a bottleneck in the development cycle.
Databricks’ new approach embeds these evaluation capabilities directly into MLflow and its Agent Bricks offering, enabling teams to shift from prototype to production with greater confidence.
Quote from Customer
“To deliver on the future of marketing optimization, we need absolute confidence in our AI agents. The make_judge API provides the programmatic control to continuously align our domain-specific judges, ensuring the highest level of accuracy and trust in our attribution modeling.” – Tjadi Peeters, CTO, Billy Grace.
Source: Databricks