AI agents are now operating inside production systems, querying Snowflake, updating Salesforce, and executing business logic autonomously. In many enterprises, they authenticate using static API keys or shared credentials rather than distinct identities in the corporate IDP.  Authenticating autonomous systems through shared credentials introduces real governance risk. When an agent executes an action, logs often... The post Identity-first AI governance: Securing the agentic workforce appeared first on DataRobot.

Moving AI agents from experimental pilots to a full-scale enterprise workforce requires more than just a model; it requires a hardware foundation that balances high-performance inference with industry-leading cost and power performance. DataRobot has technically validated the NVIDIA RTX PRO 4500 as an inference engine with a Blackwell architecture for the DataRobot Agent Workforce Platform.... The post The foundation for a governed agent workforce: DataRobot and NVIDIA RTX PRO 4500 appeared first on DataRobot.

With the arrival of NVIDIA Nemotron 3 Super, organizations now have access to a high-accuracy reasoning model purpose-built for collaborative, multi-agent enterprise workloads. Being fully open, Nemotron 3 Super can be customized and deployed securely anywhere. However, having a powerful large language model (LLM) like Nemotron 3 Super is just the starting line. The real... The post Build enterprise-ready Agentic AI with DataRobot using NVIDIA Nemotron 3 Super  appeared first on DataRobot.

When AI systems behave unpredictably in production, the problem rarely lives in a single model endpoint. What appears as a latency spike or failed request often traces back to retry loops, unstable integrations, token expiration, orchestration errors, or infrastructure pressure across multiple services. In distributed, agentic architectures, symptoms surface at the edge while root causes... The post Self-managed observability: Running agentic AI inside your boundary  appeared first on DataRobot.

Your AI agents work beautifully in the demo, handling test scenarios with surgical precision, and impressing stakeholders in controlled environments enough to generate the kind of excitement that gets budgets approved.  But when you try to deploy everything in production, it all falls apart. That gap between proof-of-concept intelligent agents and production-ready systems is where... The post Running agentic AI in production: what enterprise leaders need to get right appeared first on DataRobot.

Change is the only constant in enterprise AI. If your data workflows aren’t built to handle it, you’re setting your entire operation up for failure. Most data pipelines are brittle, breaking when data or infrastructures slightly change. That downtime can cost millions (upwards of $540,000 per hour), lead to compliance gaps that invite lawsuits, and... The post How to build resilient agentic AI pipelines in a world of change appeared first on DataRobot.

Your cash flow forecasting app is working beautifully. Your teams add their own data to keep forecasts running smoothly. Its predictions, tracking variances, and insights seem great.  …Until you take a closer look at the details, and determine that none of these systems actually talk to one another. And that’s a problem. Consolidating all of... The post How to make a cash flow forecasting app work for other systems appeared first on DataRobot.

TL;DR JointFM is the first AI foundation model for zero-shot joint distributional forecasting in multivariate time-series systems. By generating coherent future scenarios in milliseconds, it enables real-time portfolio decision-making without the lag of traditional numerical simulations. JointFM represents a paradigm shift in quantitative modeling: trained on an infinite stream of dynamics from synthetic stochastic differential... The post The digital quant: instant portfolio optimization with JointFM appeared first on DataRobot.

Your agentic AI systems are making thousands of decisions every hour. But can you prove why they made those choices? If the answer is anything short of a documented, reproducible explanation, you’re not experimenting with AI. Instead, you’re running unmonitored autonomy in production. And in enterprise environments where agents approve transactions, control workflows, and interact... The post Agentic AI observability: The foundation of trusted enterprise AI appeared first on DataRobot.

Teams building retrieval-augmented generation (RAG) systems often run into the same wall: their carefully tuned vector searches work beautifully in demos, then fall apart when users ask for anything unexpected or complex.  The problem is that they’re asking this similarity engine to understand relationships it wasn’t designed to grasp. Those connections just don’t exist. Graph... The post How to integrate a graph database into your RAG pipeline appeared first on DataRobot.