DataRobot

Looking forward to meeting customers and partners at the session tomorrow with Omnistrate and DataRobot

If you’re going to be at Dell Technologies World 2026, stop by the DataRobot booth and see why some of the world’s largest and most innovative companies are choosing DataRobot to power their agentic AI strategy. From governance and oversight to deploying and managing AI agents at scale, DataRobot helps enterprises operationalize AI in real-world, multi-cloud and on-prem environments — with the control and trust enterprises require.

DataRobot is at Dell Tech World (Booth #106) showing how enterprises are actually moving past AI pilots into a real agentic workforce — systems that automate, predict, and optimize across the business. If you're there, it's worth a stop. 👉

In AI, the release cycle moves fast. Enterprise expectations don’t. Builders want freedom. CIOs want control. Business leaders want outcomes. Winning AI platforms won’t be defined by the next demo — they’ll be defined by what still works a decade from now. That’s the principle guiding how we’re building DataRobot’s Agentic Workforce Platform: openness for builders, governance for IT, and measurable impact for the business. Proud to see this approach recognized with DataRobot being named a Top 20 AI Software Company of 2026 by CRN . 🚀 #ArtificialIntelligence #EnterpriseAI #AgenticAI #AgenticWorkforce #AIPlatforms #GenerativeAI #MLOps #ResponsibleAI #CIO #DigitalTransformation

Part 2 of my AgentOps series is live. This one is about observability — specifically what it actually takes to trace an agent run end to end, and why your existing stack probably isn't doing it. The short version: if your spans only capture LLM calls, you're missing most of the story. Tool invocations, reasoning steps, memory state, and inter-agent handoffs all need to be in the trace. Without them you're not debugging — you're guessing. A few things I cover: — Why the three pillars (metrics, logs, traces) still apply but point at a completely different target — The five layers a complete agent trace actually needs — Why OTel is the right bet right now — and where it hits a ceiling as agent systems get more complex — How to prioritize instrumentation if you're starting from zero Platforms like DataRobot have already adopted OTel as the observability backbone for agentic workflows. The enterprise side is converging on this standard. Your stack should be there too. That said — OTel was built for distributed systems, not autonomous agents. It doesn't have a native concept of memory state, belief, or governance policy. Worth watching that gap closely. Link below! #AIAgents #MLOps #Observability #SoftwareEngineering #DataEngineering

In AI, the half-life of “state-of-the-art” is about one quarter. New models. New frameworks. New agent patterns. The conversation resets constantly. Enterprise requirements don’t. THREE THINGS AREN’T CHANGING: BUILDERS WILL ALWAYS DEMAND FREEDOM They won’t accept lock-in. They choose their tools, their frameworks, and where their AI runs. CIOs WILL ALWAYS DEMAND CONTROL Without governance, visibility, and cost discipline, AI doesn’t scale. It stalls. BUSINESS LEADERS WILL ALWAYS DEMAND RESULTS Not benchmarks. Not demos. P&L impact. Three personas. Three durable expectations. Most of the AI industry is optimizing for the release cycle. Leading enterprises are starting to optimize for the decade. That’s the bet we’re making at DataRobot: openness for builders, governance for IT, outcomes for the business. Not a product pitch. A point of view on what actually lasts in enterprise AI. Are you seeing enterprises buy for what’s new — or what’s durable? #EnterpriseAI #AIPlatforms #AIGovernance #AIEngineering #TechStrategy #BusinessImpact