Editor’s note: The name of NIM Agent Blueprints was changed to NVIDIA Blueprints in October 2024. All references to the name have been updated in this blog.
AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.
The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.
An AI agent for customer service, for instance, could operate beyond simple question-answering. With agentic AI, it could check a user’s outstanding balance and recommend which accounts could pay it off — all while waiting for the user to make a decision so it could complete the transaction accordingly when prompted.
Agentic AI systems ingest vast amounts of data from multiple data sources and third-party applications to independently analyze challenges, develop strategies and execute tasks. Businesses are implementing agentic AI to personalize customer service, streamline software development and even facilitate patient interactions.
How Does Agentic AI Work?
Agentic AI uses a four-step process for problem-solving:
Learn: Agentic AI continuously improves through a feedback loop, or
“data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.
Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, visual processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
