The next area of artificial intelligence is being pointed out as 'Agentic AI'. It can autonomously solve complex multi-step problems using sophisticated reasoning and iterative planning. This is expected to improve productivity and operations across industries.

NVIDIA announced on the 31st that it is providing various tools and software to help companies build Agentic AI.

Agentic AI systems collect vast amounts of data from multiple sources, independently analyze problems and develop strategies. They also perform tasks such as optimizing supply chains, analyzing cybersecurity vulnerabilities, and helping doctors with time-consuming tasks.

■ Agentic AI operation process

Agentic AI uses a four-step process of recognition, inference, action, and learning to solve problems.

AI agents collect and process data from a variety of sources, including sensors, databases, and digital interfaces. The recognition process includes tasks such as feature extraction, object recognition, and identification of relevant objects in the environment.

The Large Language Model (LLM) then acts as an inference engine that understands the task and generates solutions, and tunes specialized models for specific functions such as content creation, vision processing, and recommendation systems. The inference step uses techniques such as retrieval-augmented generation (RAG) to access proprietary data sources and provide accurate and relevant output.

The action process means that agentic AI can quickly execute tasks according to a plan by integrating with external tools and software through application programming interfaces (APIs). In addition, guardrails can be set to ensure that AI agents execute tasks correctly.

For example, a customer service AI agent can only process claims up to a certain dollar amount, and any claims above that amount require human approval, all while executing AI tasks within guardrails.

The learning process is continuously improved through a “data flywheel,” where agentive AI feeds data generated from feedback loops or interactions into the system to improve the model. This ability to adapt and evolve more effectively over time provides businesses with a powerful tool to drive better decision-making and operational efficiency.

■ Strengthening agentic AI based on corporate data

Generative AI transforms organizations across industries and functions by turning massive amounts of data into actionable knowledge, enabling employees to work more efficiently.

AI agents access diverse data through an accelerated AI query engine to process, store, and retrieve information to enhance generative AI models. The core technology for this is RAG, which allows AI to utilize a wider range of data sources.

Thus, AI agents learn and improve by feeding back data generated through interactions to the system, creating a data flywheel. This process contributes to refining the model and increasing its effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to efficiently manage and access data, which is a key element in building responsive agentic AI applications, NVIDIA said.

■ Widely used in content, software, medical, etc.

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like creating and distributing content to more complex use cases like orchestrating enterprise software, AI agents are transforming industries.

It is expected that AI agents will accelerate their convergence across various industries, including customer service, digital humans, content creation, software engineering, and healthcare.

First, AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. More than half of service professionals reported that their interactions with customers were significantly improved, resulting in shorter response times and higher satisfaction.

There is also growing interest in digital humans, which embody a company’s brand and provide realistic real-time interactions to help salespeople answer customer inquiries or directly resolve issues when call volumes are high.

In the content creation space, agentic AI can help you quickly create high-quality, personalized marketing content. Using generative AI agents, marketers can save an average of three hours per piece of content, freeing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

In software engineering, repetitive coding tasks are automated to improve developer productivity. NVIDIA predicts that by 2030, AI will automate up to 30% of work time, freeing developers to focus on more complex tasks and drive innovation.

Finally, for doctors analyzing vast amounts of medical and patient data, AI agents can extract key insights and help them make informed treatment decisions. By automating administrative tasks and documenting clinical notes during patient visits, physicians can reduce the burden of time-consuming tasks and focus on developing relationships with patients.

Meanwhile, NVIDIA provides sample applications, reference code, sample data, tools, and comprehensive documentation through the NVIDIA NIM Agent Blueprint to accelerate the adoption of generative AI-based applications and agents.

NVIDIA partners, including Accenture, are helping enterprises deploy agentic AI with solutions built with NIM agent blueprints.