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IBM's framing centres on the operational burden of agentic AI, which runs continuously and must connect to existing enterprise systems like SAP and Salesforce, not just to new models. The article is mostly vendor positioning from an interview, so its claims about outcomes and governance are unverified. Readers evaluating enterprise agent deployments should look for evidence on data governance, human oversight, and measured business results.

Sources coveringSiliconANGLE, Snowflake Blog, AWS, NVIDIA

IBM puts agentic workloads at the heart of its AI operating model

SiliconANGLENovember 3, 20265 min read

Agentic workloads present a new operating challenge for enterprises putting artificial intelligence into production. As autonomous systems take on ongoing responsibilities, organizations must support decisions and actions across their data, applications and infrastructure. IBM Corp.’s approach draws on the experiences of clients scaling AI across their organizations. That work helped identify the requirements underpinning its […] The post IBM puts agentic workloads at the heart of its AI operating model appeared first on SiliconANGLE.

Agentic workloads present a new operating challenge for enterprises putting artificial intelligence into production. As autonomous systems take on ongoing responsibilities, organizations must support decisions and actions across their data, applications and infrastructure. IBM Corp.’s approach draws on the experiences of clients scaling AI across their organizations. That work helped identify the requirements underpinning its AI operating model, according to Ritika Gunnar (pictured), chief performance marketing officer of IBM. “As you’re more successful in using AI, especially as AI is taking action, you now have workloads that are continuously being utilized,” she…

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