From Insight to Impact: Architecting AI Infrastructure for Agentic Systems

The next frontier of AI is not just intelligent - it’s agentic. As enterprises move toward systems capable of autonomous action and real-time decision-making, the demands on infrastructure are intensifying.

In this IDC-authored blog, Madhumitha Sathish, Research Manager, High Performance Computing, examines how organizations can prepare for this shift with flexible, secure, and cost-effective AI infrastructure strategies. Drawing on IDC’s latest research, the piece highlights where enterprises stand today, and what it will take to turn agentic AI potential into measurable business impact.

Read on to learn how leading organizations are architecting infrastructure for the era of agentic systems.

Agentic AI Is Reshaping Enterprise Strategy

Artificial intelligence has become foundational to enterprise transformation. In 2025, the rise of agentic AI, systems capable of autonomous decision-making and dynamic task execution, is redefining how organizations approach infrastructure, governance, and business value. These intelligent systems don’t just analyze data; they act on it, adapting in real time across datacenter, cloud, and edge environments.

Agentic AI can reallocate compute resources to meet SLAs, orchestrate cloud deployments based on latency and compliance, and respond instantly to sensor failures in smart manufacturing or logistics. But as IDC’s July 2025 survey of 410 IT and AI infrastructure decision-makers reveals, most enterprises are still figuring out how to harness this potential.

IDC Insight: 75% Lack Clarity on Agentic AI Use Cases

According to IDC, more than 75% of enterprises report uncertainty around agentic AI use cases. This lack of clarity poses real risks where initiatives may stall, misalign with business goals, or introduce compliance challenges. Autonomous systems require robust oversight, and without well-defined use cases, organizations risk deploying models that behave unpredictably or violate internal policies.

Scaling AI: Fewer Than 10 Use Cases at a Time

IDC found that 83% of enterprises launch fewer than 10 AI use cases simultaneously. This cautious approach reflects fragmented strategies and limited scalability. Only 21.7% of organizations conduct full ROI analyses for proposed AI initiatives, and just 22.2% ensure alignment with strategic objectives. The rest rely on assumptions or basic assessments, which can lead to inefficiencies and missed opportunities.

Governance and Security: A Growing Priority

As generative and agentic AI models gain traction, governance and security are becoming central to enterprise readiness. IDC’s data shows that organizations are adopting multilayered data governance strategies, including:

• Restricting access to sensitive data
• Anonymizing personally identifiable information
• Applying lifecycle management policies
• Minimizing data collection for model development

Security testing is also evolving. Enterprises are simulating adversarial attacks, testing for data pollution, and manipulating prompts to expose vulnerabilities. Input sanitization and access control checks are now standard practice, reflecting a growing awareness that AI security must be embedded throughout the development pipeline.

Cost Clarity: Infrastructure Tops the List

AI initiatives often falter due to unclear cost structures. IDC reports that nearly two-thirds of GenAI projects begin with comprehensive cost assessments covering infrastructure, licensing, labor, and scalability. Among the most critical cost factors:

• Specialized infrastructure for training (60.7%)
• Infrastructure for inferencing (54.5%)
• Licensing fees for LLMs and proprietary tools
• Cloud compute and storage pricing
• Salaries and overhead for AI engineers and DevOps teams
• Compliance safeguards and governance frameworks

Strategic planning must account for scalability, integration, and long-term feasibility.

Infrastructure Choices: Flexibility Is Essential

IDC’s survey shows that enterprises are split between building in-house systems, purchasing turnkey solutions, and working with systems integrators. For training, GPUs, high-speed interconnects, and cluster-level orchestration are top priorities. For inferencing, low-latency performance across datacenter, cloud, and edge environments is essential.

Notably, 77% of respondents say it’s very or extremely important that servers, laptops, and edge devices operate on consistent hardware and software platforms. This standardization simplifies deployment, ensures performance predictability, and supports model portability.

Strategic Deployment: Data center, Cloud, and Edge

Inferencing workloads are increasingly distributed. IDC found that 63.9% of organizations deploy AI inference workloads in public cloud environments, while 50.7% continue to leverage their own datacenters. Edge servers are gaining traction for latency-sensitive applications, especially in sectors like manufacturing and logistics. Inferencing on end-user devices remains limited, reflecting a strategic focus on reliability and infrastructure consistency.

Looking Ahead: Agility, Resilience, and Cost-Efficient Infrastructure

As enterprises prepare for the next wave of AI innovation, infrastructure agility and governance sophistication will be paramount. Agentic AI will demand real-time responsiveness, energy-efficient compute, and resilient supply chains. IDC anticipates that strategic infrastructure planning can help in lowering operational costs while improving performance density by optimizing power and cooling demands. Enterprises can also avoid unnecessary spend through workload-aware provisioning and early ROI modeling across AI environments. Sustainability will become central to infrastructure planning, and semiconductor availability will be a strategic priority.

The future of AI isn’t just about smarter models; it’s about smarter infrastructure. Enterprises that align strategy with business value, governance, and operational flexibility will be best positioned to lead in the age of agentic intelligence.

As enterprises shift from experimentation to execution with agentic AI, AMD stands as a trusted partner in enabling this transformation. Through its leadership compute engines, open ecosystem, and full-stack solutions, AMD helps organizations operationalize AI with confidence, resilience, and agility.

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