Agentic AI is shifting the focus of infrastructure from pure model compute to the coordination of multiple specialized agents that execute complex, multi‑step workflows. This change matters to AI engineers, platform teams, SREs, and security practitioners because it introduces new CPU, networking, and data‑placement requirements, and it demands observability that can feed back into automated scheduling and human oversight.
Map the Workflow Before Selecting Hardware
Traditional AI stacks are often built around accelerators for heavy model inference. In an agentic system, each step may be a lightweight CPU task that communicates with other agents or with a human reviewer. The source advises teams to diagram both the logical workflow and the associated data flow: identify where each agent runs, what inputs it needs, where those inputs reside, and which steps can run in parallel. This mapping reveals where existing compute resources are sufficient and where additional networking bandwidth or CPU capacity is required, especially in hybrid environments that span on‑premises and cloud resources.
Observability Becomes a Scheduling Input
With several agents operating concurrently, the platform must decide which workloads receive resources first. The article points out that high‑performance computing already uses scheduling systems based on urgency and deadlines; a similar approach is needed for agentic AI. Telemetry—performance counters, storage metrics, and logs—must be exposed not only to operations teams but also to the orchestration layer that can react to latency spikes or failures. An agent encountering a performance anomaly could, for example, query the same telemetry to suggest reprioritizing its own workload or escalating to a human.
Design Around Data Movement, Not Replace It
Agents often need to pull data from multiple clouds, on‑prem systems, or external sources. The source stresses that simply having access does not guarantee timely retrieval or the correct format. Practitioners should therefore catalog data dependencies, identify latency‑sensitive sources, and consider where data should be cached or replicated to meet workflow SLAs. The assessment should start with the “as‑is” inventory of platforms, data stores, and code, looking for reuse opportunities before purchasing new technology.
Security Considerations in an Orchestrated Environment
While the source does not detail specific security controls, it implies that any system exposing telemetry to autonomous agents must ensure that only authorized components can read or act on that data. Practitioners should treat the telemetry feed as an additional attack surface and apply the same access‑control discipline used for other internal APIs.
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What This Means For Practitioners
• Begin every agentic AI project with a detailed workflow and data‑flow diagram; use it to size CPU, networking, and storage resources.
• Extend existing observability pipelines so that metrics and logs are consumable by orchestration logic, enabling dynamic priority adjustments.
• Evaluate data locality early: place frequently accessed datasets close to the agents that need them, and consider caching strategies to reduce cross‑environment latency.
• Review telemetry access controls to ensure that only trusted agents can influence scheduling decisions.
• Treat the infrastructure changes as incremental upgrades to existing platforms rather than wholesale replacements.
