Building the Enterprise Environment for Agentic AI
Intel performed thousands of agentic AI workload experiments to understand the dependencies for running agents in the enterprise environment. The findings create five practical lessons for enterprise leaders.
Intelligence analysis by Llama
Intel's experiments on agentic AI workload performance reveal the importance of considering the full system, task orchestration, data access, tool execution, latency management, governance, and scalable infrastructure. The findings create five practical lessons for enterprise leaders.
Imagine you have a team of robots that can do tasks for you. They need a special environment to work properly, with enough power, memory, and the ability to plan and scale. Intel studied how to create this environment and found some important lessons for making it work well.
Analysis
A Larger Systems Problem, Not Just One of Inference
Agentic AI is more than just a better chatbot; it is software agents that execute business tasks end-to-end across people, business workflows, data, and systems. The platform best-suited to run agents is built with proper CPU capacity, resilient data access, policy-aware tool use, observability, memory management, and the ability to predictably plan and scale agents. Intel's experiments on agentic AI workload performance reveal the importance of considering the full system, task orchestration, data access, tool execution, latency management, governance, and scalable infrastructure.
Beyond Inference: Agents as Workflow Automation
Agentic AI is more than LLM inference. Its enterprise value depends on the full system, task orchestration, data access, tool execution, latency management, governance, and scalable infrastructure. An agent is a goal-driven automated enterprise workflow process: It plans a multi-step task, calls tools, reads results, and retries when something fails. Enterprise agents are therefore not just an inference problem; they are a systems problem.
Defining What Good Looks Like
Most agentic AI metrics focus on evaluating the LLM used. Platform teams also need to know how long the tasks take, how many agents a fleet can support, what users experience at the end of the execution process, and how costs change as more agents work simultaneously. A more useful enterprise view looks at six metrics: Task success rate, Cost per task, Time per task, Task throughput, Agent density (agents per vCPU), and Latency. Together, these answer the questions enterprise AI operators care about: Is the system performing as expected? How many agents can the system sustain? How should it scale to support more agents?
Key points
- Agentic AI is a larger systems problem, not just one of inference.
- Intel's experiments on agentic AI workload performance reveal the importance of considering the full system, task orchestration, data access, tool execution, latency management, governance, and scalable infrastructure.
- A more useful enterprise view looks at six metrics: Task success rate, Cost per task, Time per task, Task throughput, Agent density (agents per vCPU), and Latency.
- Scale out by default, and scale up when agents require heavier parallel compute, shared state limits partitioning, memory locality matters, or licensing constraints apply.
If the enterprise environment for agentic AI is built correctly, it could lead to significant improvements in business operations, such as faster task completion, reduced costs, and increased productivity.
If the enterprise environment for agentic AI is not built correctly, it could lead to issues such as slow task completion, increased costs, and decreased productivity.
