Traditional enterprise Retrieval-Augmented Generation (RAG) pipelines often excel at only one type of search behavior, failing silently on others. Databricks addresses this challenge with KARL (Knowledge Agents via Reinforcement Learning), an AI agent trained simultaneously across six diverse enterprise search behaviors using a novel reinforcement learning algorithm. KARL achieves performance comparable to Claude Opus 4.6 but at 33% lower cost per query and 47% reduced latency, using synthetic data without human labeling. Its unique strength lies in “grounded reasoning,” executing complex, multi-step queries anchored strictly in retrieved facts, even when the data is fragmented and ambiguous.
Powered by OAPL (Optimal Advantage-based Policy Optimization with Lagged Inference), KARL’s training is sample-efficient, enabling enterprise-level deployment within feasible computational budgets. Unlike typical models, KARL learns to compress its own context during extended search sequences, significantly enhancing accuracy. Although it struggles with highly ambiguous questions and currently supports only vector search tasks, its multi-task reinforcement learning approach marks a significant advance over narrow, single-focus pipelines.
For data teams, KARL underscores three key considerations: adopting multi-task training for generalized search behaviors, leveraging reinforcement learning to enable transferability across diverse queries, and prioritizing RL efficiency for smarter, cost-effective search completion. KARL demonstrates that building purpose-built search agents, not just relying on broad frontier APIs, leads to smarter, more adaptable enterprise search solutions.
