Building a Robust Enterprise Environment for Agentic AI

For enterprises, agentic AI is more than an advanced chatbot; it’s about automating complex business tasks from end to end. These software agents interact with people, workflows, data, and systems, executing multi-step processes autonomously. To harness this potential, the underlying platform must be robust, scalable, and efficient. Intel's recent experiments highlight key considerations for building an enterprise environment that supports agentic AI.

Key Technical Insights

Intel conducted thousands of workload experiments to understand the dependencies and challenges of deploying agentic AI in an enterprise setting. Here are the top five lessons they’ve distilled from their findings:

• Agentic AI is a systems problem, not just inference: Unlike traditional LLMs that focus on generating text, agentic AI involves planning, tool execution, data access, latency management, and governance. Each of these components must work seamlessly for the agents to perform effectively.
• Limited existing harnesses: Most current tools for evaluating agentic AI are inadequate as they primarily measure LLM performance rather than overall system efficiency. This can lead to a misrepresentation of how well the agents will function in real-world scenarios.
• Capacity planning by density: Instead of simply counting the number of agents, plan capacity based on agents per virtual CPU (vCPU) density. This metric provides a more accurate measure of resource utilization and scalability.
• Monitor task latency, not just CPU usage: Traditional metrics like average CPU utilization don't capture the full picture. Focus on monitoring the latency of individual tasks to ensure that the system can handle real-time demands efficiently.
• Scale-out by default: For most agentic AI workloads, a scale-out approach (adding more nodes) is preferable to scale-up (increasing resources per node). Scale-up should be reserved for scenarios with heavier per-agent compute requirements or specific architectural constraints.

Under the Hood

To gain deeper insights into agentic AI workload performance, Intel extended Terminal-Bench, an open-source benchmarking tool. This extension allowed them to separate agent performance from LLM variability by using a deterministic record-replay mechanism. Here’s how it works:

• Deterministic Record-Replay: The system records LLM responses once and replays them during subsequent tests. This ensures that variations in LLM output do not affect the benchmarking results, allowing for more consistent and reliable performance metrics.
• Profiling and Telemetry: Enhanced profiling capabilities provide detailed insights into where agents spend their time. This includes tool execution, data access, and other system interactions, helping identify bottlenecks and optimize performance.
• Replay Capabilities: The ability to replay tasks allows for iterative testing and refinement of agent workflows. This is crucial for ensuring that the agents can handle a wide range of scenarios and edge cases.

Key Takeaways

Building an enterprise environment for agentic AI requires a holistic approach that goes beyond just inference capabilities. Here are the key takeaways from Intel’s findings:

• Comprehensive Metrics: Focus on metrics like task success rate, cost per task, time per task, task throughput, agent density, and latency to get a complete picture of system performance.
• Systems Thinking: Treat agentic AI as a systems problem, considering all aspects of task orchestration, data access, tool execution, governance, and infrastructure.
• Scalability and Efficiency: Plan for scalability by default, using a scale-out approach and monitoring key metrics to ensure efficient resource utilization.
• Benchmarking Tools: Utilize advanced benchmarking tools like Terminal-Bench to gain deeper insights into agent performance and identify areas for optimization.

By adopting these principles, enterprises can build robust environments that fully leverage the potential of agentic AI, driving efficiency and innovation across their operations.