Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower cost than frontier models.

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

AT&T's chief data officer shares how rearchitecting around small language models and multi-agent stacks cut AI costs by 90% at 8 billion tokens a day.

Databricks and Tonic.ai have partnered to simplify the process of connecting enterprise unstructured data to AI systems to reap the benefits of RAG. Learn how in this step-by-step technical how-to.

Bring Your Own Key (BYOK) functionality for OpenRouter allows users to seamlessly incorporate stateful capabilities ...

Abstract: The supply of emergency rescue materials plays a pivotal role in post-earthquake relief operations. However, varying disaster scenarios generate differentiated demands for emergency ...

This April will mark a decade since CMS launched the Comprehensive Care for Joint Replacement model, the federal government’s first mandatory, episode-based payment program applied broadly to ...

In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that grounds ...

The Model S and Model X were Tesla's worst-selling EVs in 2025, despite outselling some competitors. Executives say the company's future strategy is focused on autonomous vehicles and artificial ...

Google-spinoff Waymo is in the midst of expanding its self-driving car fleet into new regions. Waymo touts more than 200 million miles of driving that informs how the vehicles navigate roads, but the ...