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This collaboration addresses a critical infrastructure shift where AI agents require isolated environments for tool interaction and code execution, moving beyond pure model training. By co-designing identity management and workload controls with CoreWeave, IBM aims to balance the security needs of autonomous agents with the performance demands of large-scale H100 clusters. This partnership highlights how evolving agent architectures are driving specific requirements in cloud infrastructure engineering rather than just raw compute power.

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IBM and CoreWeave co-design controls for agent workloads

SiliconANGLEOctober 2, 20264 min read

Agent workload isolation is becoming a practical infrastructure challenge as research teams move beyond training models to running code and testing agents. Systems built for intensive computation must now accommodate workloads that interact with tools, storage and other services. IBM Research’s infrastructure must support increasingly varied workloads as its model development practices evolve. Reinforcement learning […] The post IBM and CoreWeave co-design controls for agent workloads appeared first on SiliconANGLE.

Agent workload isolation is becoming a practical infrastructure challenge as research teams move beyond training models to running code and testing agents. Systems built for intensive computation must now accommodate workloads that interact with tools, storage and other services. IBM Research’s infrastructure must support increasingly varied workloads as its model development practices evolve. Reinforcement learning adds a task-execution stage that changes what those systems must accommodate, according to Brian…

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