Authors:
Keegan Sheedy, Cloud Solutions Architect, Intel
Mihika Nerurkar, Cloud Solutions Architect, Intel
The Inference Imperative
The AI industry has reached an inflection point. After years of relentless focus on training increasingly larger language models, the economic center of gravity is shifting decisively toward inference, the production workloads that can turn AI capabilities into business value. Industry analysts predict that inference workloads will account for roughly two-thirds of all AI compute in 2026, up from a third in 2023 and half in 2025. (i)
Within inference, one category is growing faster than any other: agentic AI. Unlike simple prompt-response interactions, AI agents are autonomous systems that reason, plan, and execute multi-step tasks. They maintain context across interactions, coordinate between tools and APIs, and make real-time decisions. For enterprises, this represents a paradigm shift from AI as a tool to AI as a capable digital workforce.
Why Agents Demand a Different Infrastructure
Agentic AI workloads have fundamentally different infrastructure requirements than training or batch inference. Understanding these requirements is essential to choosing the right architecture.
Enterprise Use Cases Driving Adoption
- Intelligent customer support: Agents that understand context across interactions, search knowledge bases dynamically, escalate appropriately, and continuously improve from outcomes.
- Autonomous DevOps: Multi-agent systems that monitor production environments, diagnose issues, execute remediations, and learn failure patterns, around the clock.
- Research and analysis: Workflows that decompose complex questions, gather information from diverse sources, synthesize findings, and produce structured reports.
- Business process automation: Agents that orchestrate across enterprise systems, APIs, databases, and human approval workflows.
What unifies these workloads is their demand for sustained, concurrent inference at manageable cost. Some require real-time responsiveness, customer-facing agents, and interactive assistants that need sub-second replies. Others run as background processes, including entity extraction pipelines that scan incoming documents, offline ticket classification and routing, or batch compliance checks across contract repositories. Whether interactive or asynchronous, they need to be economical enough for constant operation and flexible enough to evolve with changing business logic.
The Case for Small Language Models on CPU
In this context, Small Language Model (SLM) refers to models with typically fewer than ~20B parameters, optimized for domain-specific reasoning, lower latency, and predictable production costs.
Large language models offer remarkable capabilities, but they impose significant tradeoffs in production agent deployments that many enterprises underestimate:
For agentic workloads, where a single task may require dozens of inference calls across planning, retrieval, reasoning, and action steps, the economics of running specialized models on your own infrastructure become compelling. Intel® Xeon® processors with Intel® Advanced Matrix Extensions (Intel® AMX), introduced starting with 4th Gen Intel Xeon processors, and supported on newer platforms, provide built-in acceleration for INT8 and BF16 inference, enabling higher throughput for agent workloads on CPU.
The Solution: Flowise on Intel Xeon Processors
Intel has built a production-ready platform for deploying AI agents on Intel Xeon infrastructure using Flowise, a popular open-source low-code tool for building agentic solutions. Flowise provides a visual, drag-and-drop interface for building sophisticated agent workflows, eliminating the need for extensive custom coding while maintaining the flexibility required for production deployments.
Architecture
The Flowise-on-Xeon Enterprise Inference Stack comprises four tightly integrated components: runtime, orchestration, workflow builder, and security, designed specifically for production-grade agent workloads. This setup automatically provisions a full Kubernetes environment alongside Flowise, enabling a scalable, resilient deployment platform.
The integrated stack brings together four key components, each optimized for enterprise agent workloads:
Figure 1: Architecture Overview
Figure 2: Example Agentic Workflow
Deployment in Three Steps
The entire stack deploys from a single configuration, targeting any Intel Xeon processor-based environment, on-premises, in the cloud, or at the edge. A typical deployment takes three steps:
- Configure credentials: Set database and admin passwords in a single configuration file.
- Enable Flowise: Toggle a single flag to include Flowise in the deployment.
- Run the deployment script: One command provisions the full Kubernetes environment with TLS, authentication, and persistent storage.
For detailed installation instructions, see the Flowise-on-Xeon deployment guide in our GitHub repository.
Real-World Impact
Organizations across industries are deploying agentic workflows on Intel Xeon processors to solve production AI challenges. The following examples help illustrate the breadth of applicable use cases and the tangible outcomes achieved.
Financial Services: Intelligent Customer Support
A financial services team building an AI-powered customer support agent needed to handle context-aware conversations, search internal knowledge bases in real time, and intelligently route complex cases to human specialists, all while keeping sensitive financial data on-premises.
Results: The visual workflow builder enabled rapid iteration on agent logic, adjusting retrieval strategies, tuning escalation rules, and refining conversation flows, without extensive re-engineering. Running on existing Xeon infrastructure with Intel® AMX acceleration, the solution met requirements while maintaining full data sovereignty.
Healthcare: Clinical Documentation Assistant
A regional healthcare system deployed an ambient documentation agent to reduce physicians' administrative burden. The agent captures clinical conversations, structures relevant information into medical notes, and integrates directly with the EHR system.
Results: Physicians reported substantial reductions in documentation time, reclaiming time for patient care. The on-premises deployment was critical; it satisfied HIPAA requirements without complex vendor data-processing agreements and delivered cost savings.
Manufacturing: Autonomous DevOps
A manufacturing company implemented a multi-agent monitoring and remediation system to provide 24/7 oversight of its production infrastructure. The agents continuously analyze telemetry, correlate alerts, diagnose root causes, and execute predefined remediation playbooks.
Results: The AI-powered solution monitors infrastructure health, diagnoses issues, and can execute automated remediation workflows, reducing the operational burden on IT teams. Running on existing Intel Xeon infrastructure, the deployment required no incremental hardware investment.
Getting Started
Whether organizations are evaluating agentic AI for the first time or scaling an existing deployment, the Flowise-on-Xeon stack can provide a clear path from proof of concept to production.
- Identify a high-value agent use case: Start with a workflow that has clear ROI: repetitive, knowledge-intensive, or latency-sensitive tasks.
- Deploy the stack: Use the three-step deployment process on the Intel Xeon platform.
- Build and iterate: Use Flowise’s visual builder to prototype, test, and refine agent workflows with rapid feedback cycles.
- Scale with confidence: Leverage Kubernetes orchestration to scale horizontally as usage grows, with predictable infrastructure costs.
Link to GitHub Repository: Flowise on Xeon
The Bottom Line
The AI narrative is shifting, from training to inference, from monolithic models to specialized ones, from GPU-only architectures to intelligent infrastructure decisions. Agentic AI is the next frontier: autonomous systems that reason, plan, and act on behalf of your organization.
The enterprises that lead in AI will be those that deploy inference efficiently, iterate rapidly, and scale sustainably.
With Flowise on Intel Xeon processors, enterprises are ready to help build that future today.
Support: Contact your Intel Solutions Architect or visit intel.com/ai to get started.
Footnotes:
(i) Inference compute share projections: Deloitte, "More compute for AI, not less," Technology, Media, and Telecommunications Predictions 2026, November 2025. Deloitte estimates inference workloads accounted for approximately one-third of all AI compute in 2023, half in 2025, and projects roughly two-thirds in 2026.
https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2026/compute-power-ai.html
(ii) Performance varies by workload and configuration. Supermicro benchmarking of 5th Gen Intel Xeon processors demonstrated up to 3.7x–4.2x improvements in inference throughput with Intel AMX enabled across BERT-Large NLP and image recognition workloads, with gains up to 12.5x for object detection inference. See: Supermicro, "AI Inference Performance with 5th Gen Intel Xeon and Intel AMX," 2024.
https://www.supermicro.com/products/brief/product-brief-X13-5thGen-AMX.pdf
Notices and Disclaimers
Performance varies by use, configuration, and other factors. Learn more on the Performance Index site.
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product or component can be absolutely secure.
Your costs and results may vary.
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