Building the enterprise environment for agentic AI
Agentic AI offers enterprises more than chatbots, enabling end-to-end task execution across workflows and systems. Intel’s research provides practical lessons for building the necessary infrastructure, focusing on system performance, scaling, and observability beyond just LLM metrics.
Agentic AI represents a significant leap for enterprises beyond simple chatbots, acting as software agents that execute business tasks end-to-end across people, workflows, data, and systems. Building an effective platform for these agents requires robust CPU capacity, resilient data access, policy-aware tool use, observability, memory management, and the ability to predictably plan and scale. Intel’s extensive experiments have yielded five practical lessons for enterprise leaders in this area.
The value of agentic AI hinges on the entire system, encompassing task orchestration, data access, tool execution, latency management, governance, and scalable infrastructure. Agents are essentially goal-driven automated enterprise workflow processes. They plan multi-step tasks, utilize tools, interpret results, and self-correct upon failure. This means that for enterprises, agentic AI is fundamentally a systems problem, not merely an inference problem.
Effective deployment of agentic AI involves carefully planned phases. Instead of focusing solely on agent count, enterprises should consider agent density—agents per vCPU—as a primary signal for saturation. Scaling out, which involves adding more systems to increase total agent capacity, is generally the preferred approach over scaling up, which adds resources to a single system. This strategy aligns with the semi-independent nature of agents and their modest, bursty compute demands.
A new approach to observability is also crucial. Traditional average CPU utilization is insufficient for agentic workloads due to their "bursty" nature. Task latency (P95) offers a more effective leading metric, as it indicates when workflows are experiencing delays even before average task duration significantly degrades. This allows for proactive identification and resolution of performance bottlenecks.
The business implications of agentic AI are significant. The most successful implementations are seen in organizations that integrate an automation layer into existing workflows with defined rules and measurable service levels, such as code creation, regression testing, or market analysis. The true value lies in enabling businesses to complete real work across teams and systems, improving productivity, ensuring compliance, and scaling operations efficiently, rather than simply optimizing model performance.
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