Creating AI agents is easier (and faster) than you think

This year, the conversation in the C-suite has shifted from agentic AI to, “How do we make AI work safely, repeatedly, and at scale?” The answer lies in AI agents, autonomous or semi-autonomous systems that don’t just chat, but reason, plan, and execute multi-step tasks across your enterprise ecosystem.

For many CIOs, the perceived barrier to agentic AI is a mountain of custom coding and infrastructure complexity. But thanks to the deep co-engineered innovation between HPE and NVIDIA, that mountain has become a molehill. By leveraging the HPE AI factory with NVIDIA, building and deploying production-ready agents is now a matter of clicks, not months.

The shift from chatbots to intelligent agents

While first-generation AI focused on single-turn interactions, like a basic chatbot answering a question, agentic AI represents a fundamental evolution. An agent can:

• Reason: Break down a complex goal (“Optimize our Q3 supply chain”) into actionable steps.
• Use tools: Access your SQL databases, ERP systems, or external APIs to pull real-time data.
• Act: Execute the final task, such as generating a purchase order or updating a CRM entry.

Making it practical: The blueprint approach

The secret to speed is not building from scratch; it’s building on what is already proven.  NVIDIA’s NIM Agent Blueprints provide preconfigured, reusable reference workflows for common enterprise use cases, with the required microservices sample code and deployment guides built-in.

HPE provides the optimized infrastructure to run these blueprints seamlessly, delivering a standardized, high-performance environment that comes with all the compute, storage, and networking needed to run these agents, maximize output, and minimize latency.

3 production-ready AI agents you can use today

For CIOs, the key question is not whether an agent can demo well. It is whether the agent can access the right data, explain its work, operate within security guardrails, and scale from a team workflow to an enterprise service. To get started, explore the following available blueprints.

1. The knowledge extraction agent (Multimodal PDF data extraction)

Enterprises are drowning in unstructured data, including financial reports, contracts, and technical manuals. This agent uses a combination of vision and language models to read PDFs, including tables and charts, and turn them into actionable insights.

• Business value: Automate quarterly earnings analysis or contract audits.
• Try it: Explore the Multimodal PDF Extraction Blueprint on the NVIDIA API catalog.

2. The enterprise search and analysis agent (AI-Q)

Built with LangChain, the NVIDIA AI-Q Blueprint is an open reference architecture for enterprise agentic search. It helps teams build agents that can search across approved knowledge sources, show how answers were produced, and can be customized for internal data and governance requirements.

• Business value: Empower your internal teams with a super-analyst that knows every document in your company.
• Source code: Available on the NVIDIA AI Blueprints GitHub.

3. The digital human for customer service

This blueprint combines GenAI with animation technology to create a lifelike digital interface that interacts with customers in real-time, resolving problems rather than redirecting them to a help article.

• Business value: Transform high-volume customer service desks into 24/7, high-fidelity experiences.
• Get started: View the Digital Human Blueprint details and requirements.

Case study: Transforming municipal services in the Town of Vail

The Town of Vail example shows how agentic AI becomes operational. Using the HPE Agentic Smart City Solution with NVIDIA and Kamiwaza, Vail can automate labor-intensive document and resident-service workflows while keeping control of sensitive municipal information.

• The challenge: Staff were burdened by manual processes, such as deciphering deed restrictions for affordable housing and responding to repetitive visitor questions.
• The solution: With the HPE Agentic Smart City Solution, the town deployed specialized agents to automate data-heavy tasks.
• The outcome: The deed restriction agent alone saved nearly a full-time employee’s annual workload, allowing staff to focus on high-level community planning. Furthermore, a 24/7 AI-powered avatar now provides residents and tourists with instant, accurate information.

Moving from concept to reality

The winner in the agentic era won’t be the company with the most pilots, but the one that puts agents into production first. By leveraging the NVIDIA Agent Toolkit and HPE’s purpose-built AI infrastructure, many of the technical barriers have been removed.

Building an agent isn’t just about the code; it’s about the data flywheel, where your agents learn and improve over time by interacting with your specific business processes.

Next step for CIOs: Jump-start your team’s AI capabilities with free agentic AI courses and learn more about HPE AI Factory with NVIDIA at https://www.hpe.com/ai.

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As AI becomes increasingly central to economic competitiveness, scientific advancement, and national priorities, organizations require infrastructure that balances performance with security and sovereign control. Together, HPE and NVIDIA co-engineer rack-scale AI systems that integrate AI computing, high-performance networking, and supercomputing expertise to support large-scale AI workloads. This provides enterprises, governments, and research institutions with a trusted foundation for sovereign AI initiatives while maintaining control over critical data, models, and operations.