Introduction
Agentic AI is the next evolution in artificial intelligence (AI), enabling systems to reason, act, and learn autonomously. Unlike traditional AI, Agentic AI agents can break down complex problems, plan iteratively, and adapt over time.
At HPE Discover 2025, I had the opportunity to present my vision for the future of artificial intelligence with Agentic AI. A paradigm shift that builds upon and surpasses traditional AI models.
This article is based on the presentation and the feedback I received at the conference.
You can find the link to the full presentation here.
From Generative to Agentic: The Evolution of AI
The evolution of AI from rule-based systems to machine learning (ML), deep learning (DL), and generative AI (GenAI). Generative AI provides us with large language models (LLMs), marking a significant leap by enabling machines to generate content such as text, images, audio, and more rather than merely recognizing or classifying it.
Figure 1 – Evolution of Artificial Intelligence – NVIDIA ©
However, GenAI models by themselves have limitations: they predict the next word, often hallucinate, rely on stale data, and are challenging to integrate with tools. These shortcomings paved the way for Agentic AI, which builds on GenAI’s foundation but introduces autonomy, adaptability, decision-making capabilities, and adds a clear path to add more useful use cases.
What Is Agentic AI?
Agentic AI represents a new class of intelligent systems capable of perceiving, reasoning, acting, and learning autonomously. Unlike traditional AI, which often requires human orchestration, Agentic AI can break down complex, multi-step problems, plan iteratively, validate outcomes, and adapt over time.
Figure 2 – What is Agentic AI?
These systems are goal-oriented and often operate with other agents.
What is the role of humans in Agentic AI? Humans are responsible for designing agentic workflows, defining prompts, and tuning models to ensure agents perform effectively.
NVIDIA outlines a four-step process for Agentic AI workflows:
- Perceive – Aggregate from data sources such as sensors and vector databases, extracting critical features and identifying new objects or entities within their operational domain.
- Reason – Use LLMs to orchestrate tasks, generate solutions, and access domain-specific knowledge via Retrieval-Augmented Generation (RAG).
- Act – Rapidly complete tasks with external tools via APIs. Built-in safeguards help ensure proper execution. Establishing thresholds for when human approval or support is needed to move forward.
- Learn– Improve through feedback loops, ingesting generated data to refine models and enhance performance. NVIDIA refers to this as the “data flywheel,” and the ability to adapt and drive for better decision-making and operational efficiency.
Enterprise Use Cases: Agentic AI in Action
Agentic AI is rapidly being adopted across all market sectors, including finance, healthcare, manufacturing, retail, and automotive. Its ability to autonomously perceive, reason, act, and learn makes it a transformative force, enabling tailored solutions and operational efficiencies in diverse industries.
Large enterprises have already integrated Agentic AI in their business workflows with success:
- IT Operations: Agents monitor infrastructure, detect anomalies, and resolve incidents, improving uptime by 15% and reducing support costs by 20%.
- Marketing: AI agents plan and optimize campaigns, personalize messaging, and generate content, boosting ROI by 20% and halving content creation time.
- Customer Service: Autonomous agents manage inquiries across channels, escalate complex cases, and learn from interactions, cutting response times by 70% and increasing customer satisfaction.
- R&D: In pharmaceuticals and life sciences, agents design experiments, analyze results, and iterate hypotheses, accelerating discovery cycles by 50%.
These examples underscore Agentic AI’s ability to drive measurable business outcomes through intelligent automation.
Enabling Agentic AI in the Enterprise
How do organizations start with Agentic AI?
Successful adoption begins with a plan that connects technical skills to measurable results and responsible innovation.
Strategic roadmap:
- Identify High-Impact Use Cases – Focus on domains where autonomy and adaptability can deliver tangible value.
- Build Modular Infrastructure – Invest in platforms that support agent orchestration and scalable deployment.
- Establish Governance & Guardrails – Define ethical boundaries and compliance protocols.
- Pilot with Purpose – Launch controlled pilots with clear KPIs and feedback loops.
- Upskill Teams – Train staff in prompt engineering, agent design, and AI operations.
- Scale Strategically – Expand based on pilot insights, prioritizing interoperability and continuous learning.
At HighFens, we have written a separate article on getting started with Agentic AI using the NVIDIA AI Workbench. Follow the link here if you prefer a hands-on approach to getting started.
Figure 3 – Getting Started with NVIDIA AI Workbench
Agentic AI demos
The presentation included two demos based on the NVIDIA Digital Human blueprint.
Two digital human agents, Aria and James, highlight conversational AI in action.
Aria engaged in empathetic, human-like dialogue, while James managed technical queries about Agentic AI and HPE’s role in its development. The demos illustrate how Agentic AI can power intelligent, interactive avatars for customer service and beyond.
You can watch the demos on YouTube by clicking on their respective pictures.
| ARIA | JAMES |
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Concluding thoughts
Agentic AI is the next frontier in AI evolution. By combining reasoning, autonomy, and adaptability, Agentic AI moves beyond action assistance, transforming how enterprises operate, innovate, and serve customers. Start small, think big, identify a high-impact use case, pilot with purpose, and scale with confidence.
Finally, I would like to thank everybody who joined the session, HPE, and Connect Converge for allowing me to present at HPE Discover 2025.
