AMD EPYC Server CPUs
No matter the size or scale of your AI deployments, AMD EPYC Server CPUs give you a high-performance, energy-efficient foundation for enterprise AI and general-purpose workloads.
The Era of Agentic AI Has Arrived
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Generative AI introduced systems that could reason and respond.
Agentic AI augments these systems to plan, act and complete tasks.
In Agentic AI, the agent takes action. It transforms goals into plans, retrieves and processes data, triggers inference, uses software tools and finally validates results, iterating through stages until the task is completed successfully.
In practice, Agentic AI is more than something you “chat with.” It is a digital assistant that helps complete tasks —triggered by a user request, a scheduled job, an event, or another agent—by operating software on your behalf. Coding agents, research agents, IT automation agents, and business process agents are early examples of agents being employed to complete tasks.
This shift from conversation to action fundamentally changes the balance of compute. Agents run on CPUs, thereby changing the infrastructure requirements for AI by exponentially increasing CPU compute demand. Though Agentic AI can increase CPU usage, it may also increase GPU usage. Agentic AI typically uses AI inference (GPU-heavy workload) for intelligence and Tools (CPU-heavy workloads) to perform tasks.
From Models to Systems: What Makes AI “Agentic”
Traditional AI deployments center on a prompt/response loop where: a user provides a prompt, and the AI model provides a response. Agentic AI refers to systems with agency—the ability to take actions independently within defined boundaries. Instead of a single prompt/response loop, agents expand that loop into a system:
- Request and Tokenize: decomposition of the input or prompt, initiation of agentic session
- Plan and Retrieve: layers that bring enterprise knowledge into context and decide what to do next
- Reason and Generate: primary model inference that delivers a response to the Agent, or provides further instructions to the Agent to complete the task
- Tool Execution: layers that act across software, services, and APIs to take action
- Verify and Respond: ensure response completeness and correctness and delivers result to user or iterates processes if needed
This unique looping pattern is transforming business processes. AI systems now act as a corpus of jobs. A single request can spawn many agents, each operating independently but within defined constraints, interacting with software, data, and services, then dissolving when their task is complete.
Why Agentic AI Increases the Importance of the CPU
Agents run on CPUs. Each agent also need multiple CPUs to handle all the tasks the agent creates. As agentic AI scales, the work that happens outside of the GPU increases:
- Coordinating many concurrent agents
- Managing system state and memory
- Connect and interact with enterprise software
- Handling control path logic and I/O
To support all of these, there are three fundamental types of CPU roles in Agentic systems
AI Host CPU
Responsible for data pre and post processing to maximize efficiency of the GPU running Inference. AMD EPYC 9005 high frequency CPUs enable exceptional GPU efficiency.
Agent CPU
Responsible for hosting the agent framework. Coordinate all tasks across CPUs and GPUs, including policy controls like identity control, budget and prioritization. AMD EPYC 9005 CPUs, specifically high core count offerings, deliver the compute capacity needed to scale up agentic workloads.
Tool CPU
Responsible for task execution across standard enterprise platforms such as databases, storage, compute, search, etc. spawned by multiple agents. Tool CPUs exist in a variety of CPU and server configurations. AMD EPYC 9005 Server CPUs offer a comprehensive set of performance, core count and price points to support virtually any size and scale.
What Makes a Good Agent CPU?
High core counts to scale concurrent agents and enable high task execution
Power Efficiency to maximize agent capacity in your datacenter
Cost Efficiency to scale agents to meet demand to minimize TCO
A mature software ecosystem for enterprise tools and frameworks
These requirements reflect why agentic AI is fundamentally a general-purpose computing paradigm.
AMD EPYC Server CPUs are Ideal for Agentic AI
AMD EPYC™ 9005 Series Server CPUs stand out as the premier choice for supporting the full range of agentic AI workloads because they deliver:
- Leadership core density to run many agents in parallel
- Optimal power efficiency with the leading vCPU count per TDP
- Ideal cost efficiency with high core count per TCO
- Native compatibility with robust enterprise x86 software ecosystem
AMD EPYC Server CPUs allow customers to match infrastructure to diverse workload needs—using high core count CPU capacity where throughput dominates and high-performance cores where responsiveness matters—without forcing agentic AI into a one-size-fits-all design.
FAQs About Agentic AI
Agentic AI typically works by combining reasoning, planning, tool use, memory, and feedback. An AI agent receives a goal, creates a plan, selects the right tools or data sources, performs actions, checks the outcome, and adjusts as needed. Depending on the use case, it may connect to business applications, databases, APIs, email, chat platforms, or workflow systems.
Generative AI creates content such as text, images, code, summaries, or recommendations. Agentic AI goes further by taking action. It can break a goal into steps, decide what to do next, use software tools, retrieve information, complete tasks, and monitor progress. In most cases, Agentic AI uses Generative AI, to provide the intelligence as part of the larger workflow.
Agentic AI can support complex workflows across many business functions. Common use cases include customer service, sales support, research, software development, data analysis, IT operations, marketing, HR, finance, and back-office automation. For example, an AI agent could research a prospect, update a CRM, draft a follow-up email, and schedule the next step.
Agentic AI can help organizations increase productivity, reduce repetitive work, speed up decision-making, and improve customer and employee experiences. Because it can manage multi-step processes, it is especially useful for work that requires coordination across systems, teams, or data sources. It can also help employees focus on higher-value tasks by handling routine or time-consuming activities.
Agentic AI can be safe and effective when it is designed with the right controls. Important safeguards include human oversight, clear permissions, data security, audit trails, testing, approval workflows, and limits on what actions an agent can take.