Key Capabilities Checklist: Your Path to Operational Readiness for Agentic AI

AI can’t deliver enterprise value if it’s limited to SaaS apps or run only in the public cloud, where costs can quickly spike and management grows complex. To seek a better ROI, enterprises need a platform approach that treats AI with the same rigor as any mission-critical application. A platform approach embeds automation, observability, and governance into operations so that models run consistently and predictably across data center, cloud, and edge.

Key capabilities include:

• Automated operations and lifecycle management for purpose-built AI workloads.
• Enterprise-grade observability and governance to provide trust and consistency.
• Streamlined distribution of training, fine-tuning, and inference across hybrid environments.
• Policy-based placement to balance performance, cost, and sovereignty compliance.

The future of AI lies in agentic systems, where multiple agents interact with data, systems, and each other in real time. These workloads demand orchestration, modular execution, and dynamic scaling that traditional infrastructure cannot provide. Without enterprise-grade orchestration and governance, these systems overwhelm IT and amplify risk.

Key capabilities include:

• Orchestration tools to coordinate multi-agent systems in real time and seamlessly scale them across datacenter, cloud, and edge.
• Modular execution environments that support dynamic scaling.
• Helps secure interactions between AI agents and enterprise systems.
• Elastic infrastructure spanning datacenter, cloud, and edge to support real-time responsiveness.

Enterprises face tough choices when planning AI infrastructure. Should inference run in the cloud for elasticity, on-prem for data sovereignty, at the edge for low-latency needs, or a mix of all three? Meanwhile, IT must balance GPUs, CPUs, and emerging accelerators while navigating compounding AI token usage as well as power, cooling, and hardware server supply chain constraints. A platform approach provides the agility to right-size resources, helps drive a more seamless adaptation to new hardware adapt to new hardware, and extends AI workloads reliably across supported environments—helping mitigate operational waste, or designed to help avoid compute bottlenecks, and costly deployment delays.

Key capabilities include:

• Unified access to GPUs, CPUs, and emerging accelerators.
• Intelligent workload placement and optimized AI token utilization and scalable AI infrastructure across hybrid multicloud environments to avoid bottlenecks and waste.
• High-bandwidth infrastructure to support demanding training and inference.
• Flexibility to extend inference workloads to the edge for responsiveness and sovereignty.