Meet KARL: A Faster Agent for Enterprise Knowledge, powered by custom RL

2026-03-05 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

Databricks has developed KARL, a custom reinforcement learning (RL) model designed to power enterprise knowledge agents, specifically for grounded reasoning tasks. KARL matches the performance of leading proprietary models while significantly reducing inference costs and latency, achieving this with only a few thousand GPU hours of training and entirely synthetic data. This model addresses the challenge of hard-to-verify tasks, where there is no single correct answer, making RL guidance particularly difficult. KARL's capabilities are being integrated into Databricks products like Agent Bricks Knowledge Assistant, improving responses for human users. The underlying RL pipelines and infrastructure used to create KARL are now available to Databricks customers through a private preview, enabling them to build their own efficient, domain-specific custom RL models for high-volume agentic workloads.

Key takeaway

For CTOs and VPs of Engineering scaling AI agents, consider adopting custom reinforcement learning to significantly reduce inference costs and latency while maintaining or improving model quality. Your teams can leverage Databricks' new custom RL pipelines to develop domain-specific agents, especially for hard-to-verify grounded reasoning tasks, moving beyond reliance on expensive frontier models.

Key insights

Custom reinforcement learning can drastically improve enterprise agent performance while reducing costs.

Principles

• RL can strictly dominate frontier models.
• Synthetic data can train powerful RL agents.

Method

Databricks used custom RL techniques and infrastructure, along with synthetic data, to train KARL for grounded reasoning tasks, matching proprietary model performance at lower cost.

In practice

• Optimize high-volume agentic workloads with RL.
• Build domain-specific agents with custom RL.
• Reduce inference costs for enterprise agents.

Topics

• Reinforcement Learning
• Enterprise AI Agents
• Grounded Reasoning
• Inference Cost Optimization
• Custom Model Training

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.