Agentic AI uses complex reasoning and iterative planning to autonomously solve complex, multi-step problems.

AI chatbots use generative AI to provide responses based on single interactions. A person asks a question, and the chatbot replies using natural language processing.

The next frontier of artificial intelligence is agentic AI, which uses complex reasoning and iterative planning to autonomously solve complex, multi-step problems, which will improve productivity and operational efficiency across industries.

Agentic AI systems acquire massive amounts of data from multiple sources, independently analyze challenges, formulate strategies, and perform tasks such as supply chain optimization, cybersecurity vulnerability analysis, and assisting doctors with time-consuming work.

How Does Agentic AI Work?

Agentic AI uses a four-step process to solve problems:

Perception: The AI agent collects and processes data from various sources such as sensors, databases, and digital interfaces. This includes extracting meaningful features, identifying objects, or determining relevant entities in the environment.

Reasoning: Large language models act as orchestrators or reasoning engines, understanding tasks, generating solutions, and coordinating specialized models for specific functions such as content creation, visual processing, or recommendation systems. This step uses techniques such as Retrieval Augmented Generation (RAG) to access proprietary data sources and provide accurate and relevant outputs.

Action: Through application programming interfaces with external tools and software, agentic AI can quickly execute tasks according to its formulated plan. Guardrails can be established within the AI agent to help ensure correct execution. For example, a customer service AI agent can handle claims up to a certain amount, while claims exceeding this amount must be approved by a human.

Learning: Agentic AI continuously improves through feedback loops or "data flywheels," feeding the data generated from interactions back into the system to enhance the model. This ability to adapt and become more effective over time provides businesses with a powerful tool to improve decision-making and operational efficiency.

Leveraging Enterprise Data to Drive Agentic AI

Across various industries and job functions, generative AI is transforming business organizations by helping employees work more efficiently by turning vast amounts of data into actionable knowledge.

AI agents realize this potential by accessing various data through accelerated AI query engines that process, store, and retrieve information to enhance generative AI models. A key technology to achieve this is RAG, which enables AI to leverage a broader range of data sources.

Over time, AI agents learn and improve by creating data flywheels, where data generated through interactions is fed back into the system, 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 essential for building responsive agentic AI applications.

Practical Applications of Agentic AI

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

Customer Service: AI agents improve customer support by enhancing self-service capabilities and automating routine communications. Over half of professional service personnel report significant improvements in customer interaction, reducing response times and increasing satisfaction.

There is also growing interest in digital humans — AI-powered agents that embody a company's brand and provide realistic, real-time interactions, helping sales representatives answer customer inquiries or directly resolve issues during peak call times.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers time, an average of three hours per piece of content, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can remain competitive while increasing customer engagement.

Software Engineering: AI agents increase developer productivity by automating repetitive coding tasks. It is projected that by 2030, AI could automate up to 30% of working hours, allowing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can extract key information to help them make more informed care decisions. Automating administrative tasks and recording clinical notes during patient appointments can reduce the burden of time-consuming tasks, allowing doctors to focus on building patient relationships.

AI agents can also provide 24/7 support, providing information on prescription drug use, appointment scheduling, and reminders, helping patients adhere to treatment plans.

Getting Started

With its ability to plan and interact with various tools and software, agentic AI marks the next chapter in artificial intelligence, offering the potential to increase productivity and revolutionize how business organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, the NVIDIA NIM™ Agent Blueprint provides sample applications, reference code, sample data, tools, and comprehensive documentation.

NVIDIA partners, including Accenture, are helping businesses use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com for more details about the tools and software NVIDIA offers to help businesses build their own AI agents.

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