For AI developers, the challenge has shifted from building an agent to orchestrating, governing, and scaling it in a production environment. DataRobot’s latest release introduces a robust suite of tools designed to streamline this lifecycle, offering granular control without sacrificing speed.
New features in DataRobot 11.2 and 11.3 help you close the gap with dozens of updates spanning observability, developer experience, and infrastructure integrations.
Together, these updates focus on one goal: reducing the friction between building AI agents and running them reliably in production.
The most impactful areas of these updates include:
The sections that follow focus on these capabilities in detail, starting with standardized connectivity, which underpins every production-grade agent system.
Agents break when tools change. Custom integrations become technical debt. The Model Context Protocol (MCP) is emerging as the standard to solve this, and we’re making it production-ready.
We’ve added an MCP server template to the DataRobot community GitHub.
Everyone is building RAG. Almost nobody is building RAG with RBAC, audit trails, and the ability to swap models without rewriting code.
The “Talk to My Docs” application template brings natural language chat-style productivity across all your documents and is secured and governed for the enterprise.
Getting an agent into production should not require days of scaffolding, wiring services together, or rebuilding containers for every small change. Setup friction slows experimentation and turns simple iterations into heavyweight engineering work.
To address this, DataRobot is introducing an agentic application starter template and CLI, both designed to reduce setup overhead across both code-first and low-code workflows.
As prompts move from experiments to production assets, ad hoc editing quickly becomes a liability. Without versioning and traceability, teams struggle to reproduce results or safely iterate.
To address this, DataRobot introduces the Prompt Management Studio, bringing software-style discipline to prompt engineering.
As AI agents scale across teams and workloads, visibility and control become non-negotiable. Without clear insight into resource usage and enforceable limits, performance bottlenecks and cost overruns quickly follow.
As teams experiment with agent behavior and reasoning, access to multiple foundation models becomes essential. Managing separate credentials, rate limits, and integrations across providers quickly introduces operational overhead.
Jira and Confluence connectors: To power your vector databases, DataRobot provides a cohesive ecosystem for building enterprise-ready, knowledge-aware agents.
NVIDIA NIM Integration: Deploy Llama 4, Nemotron, GPT-OSS, and 50+ GPU-optimized models without the MLOps complexity. Pre-built containers, production-ready from day one.
Milvus Vector Database: Direct integration with the leading open-source VDB, plus the ability to select distance metrics that actually matter for your classification and clustering tasks.
Azure Repos & Git Integration: Seamless version control for Codespaces development with Azure Repos or self-hosted Git providers. No manual authentication required. Your code stays centralized where your team already works.
If you’re already a customer, you can spin up the GenAI Test Drive in seconds. No new account. No sales call. Just 14 days of full access inside your existing SaaS environment to test these features with your actual data.
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For more information, please visit our Version 11.2 and Version 11.3 release notes in the DataRobot docs.