A practical guide for platform teams managing shared AI deployments

UIUC and Chroma's Harness-1 is a 20B retrieval subagent trained with reinforcement learning inside a stateful search harness. The harness maintains the bookkeeping — candidate pool, importance-tagged curated set, evidence graph, verification records — while the policy decides what to search, curate, verify, and when to stop. It reaches 0.730 average curated recall across eight benchmarks, beating the next open subagent by 11.4 points and trailing only Opus-4.6. Weights and harness code are public. The post Meet Harness-1: A 20B Retrieval Subagent Trained With Reinforcement Learning Inside a Stateful Search Harness on gpt-oss-20b appeared first on MarkTechPost.

The third post from Build Club, our weekly live build session. The companion GitHub repo can be found here, docs here and you can try the agent live in the hosted playground. Your agent framework is not the bottleneck. The bottleneck is that every new external system your agent needs to talk to requires another... The post Build an agent that writes its own tools appeared first on DataRobot.

The second post from Build Club, our weekly live build session. A companion GitHub repo can be found here. Your inbox is not the problem. The problem is that you are the person other people are waiting on. Some of those messages need you specifically. Most of them need an answer you have already given... The post Build a digital twin agent (with guardrails) appeared first on DataRobot.

Every LLM deployment has a ceiling, a latency curve, and a unit cost. Most teams operate blindly, discovering their deployment limits only when over-provisioning exhausts their GPU budget or peak traffic causes a catastrophic failure. Three numbers matter: maximum sustained concurrency before GPU saturation, end-to-end latency at that concurrency, and cost per million tokens at... The post Industry-standard LLM benchmarks in DataRobot appeared first on DataRobot.

The hardest part of building against a new platform is teaching your tools about it. Your coding agent doesn’t know the SDK’s conventions. Your IDE doesn’t know the CLI commands. Your terminal doesn’t know the auth pattern. Every gap is a context switch, and every context switch is time spent away from the work. DataRobot... The post DataRobot for Developers: Skills in Cursor, Gemini, and Claude appeared first on DataRobot.

You shouldn’t have to leave Cursor to build, deploy, or monitor a production-grade agent. You can wire together LangChain, a vector DB, a monitoring tool, and a deployment pipeline yourself, but you’ll spend more time on that plumbing than on the agent itself. DataRobot is the shortcut. It now lives where you build, integrating directly... The post DataRobot for Developers: Skills, MCP, and the agentic developer surface appeared first on DataRobot.

The race to production-ready agentic AI is on — but for most enterprises, the finish line keeps moving. Models get built, pilots get run, and then teams hit a wall: the infrastructure, security, governance, and operational requirements for running AI agents at enterprise scale are far more complex than any single tool or vendor anticipated.... The post Building the enterprise agentic AI factory with DataRobot and Dell appeared first on DataRobot.

This is Build Club. We’ve been running it for two months. It is the single highest-signal hour of our week, and it is genuinely easy to copy. Every Friday afternoon, twenty-something DataRobot employees pile into a Google Meet. Someone shares their screen. They start typing. There are no slides, no demo script, no agenda beyond... The post A playbook to run an agent Build Club appeared first on DataRobot.