What is agentic AI?

Find out how one agentic AI assistant can elevate the way everyone works.

How does agentic AI work?

Agentic AI refers to AI systems designed to autonomously pursue complex goals and execute multi-step workflows with minimal human supervision. It demonstrates autonomous decision-making, strategic planning, and adaptive execution to complete processes that traditionally require human reasoning.

At its core, agentic AI aims to function more like a capable employee. It understands natural language instructions, interprets context, sets appropriate goals, breaks down tasks into subtasks, and adjusts its actions based on new information or changing conditions. It continuously reasons about the best next step to move toward a desired outcome.

Its key capabilities include autonomy, advanced reasoning, adaptive planning, natural language understanding, and workflow optimization. These abilities allow agentic AI to operate proactively and intelligently in dynamic environments while handling complex objectives end to end.

Why is agentic AI important?

Agentic AI marks a major evolution beyond traditional narrow AI systems that can only perform rigid, predefined tasks. By combining large language models, massive training datasets, scalable compute, and enterprise connectivity, agentic AI can set its own goals, plan efficient workflows, make nuanced decisions, and adapt in real time. This sophisticated autonomy and contextual reasoning make it possible to reliably automate complex enterprise workflows with far less human oversight.

Why does agentic AI matter for companies?

Conventional AI still depends on significant human supervision, limiting its ability to automate advanced processes. Agentic AI addresses this gap by enabling AI assistants to independently manage responsibilities once handled exclusively by employees. This can generate major efficiency gains by automating complex, multi-step workflows and allowing employees to focus on higher-value work.

Agentic AI also enables more optimized, real-time operations that can adapt on the fly. For forward-thinking companies, implementing agentic AI has the potential to redefine roles, reshape workflows, and transform how teams collaborate with AI across the organization.

Explore More

Expand your AI knowledge—discover essential terms and advanced concepts.

Extensibility

Extensibility in AI means enabling systems to grow their abilities across new domains, tasks, or datasets without complete retraining or significant modifications.

Extraction

Extraction allows generative models to examine vast datasets and identify meaningful patterns, and precise information relevant to a given goal or context.

Few-Shot Learning

Few-shot learning is a machine learning technique that enables models to understand new concepts from only a handful of labeled samples, fewer for each category.

Fine Tuning

Fine-tuning uses a pre-trained model and adapts it for a specific task with a smaller dataset. A model trained on intersections can learn to detect red-light violations.

Foundation Model

Foundation models are versatile AI systems that include large language, computer vision, and reinforcement learning models. They’re termed “foundation”.

GPT-3

GPT-3 is the third model in the GPT-n series, built with 175 billion parameters that help it make predictions. ChatGPT runs on GPT-3.5, an advanced version designed for smoother, smarter responses.

GPT-4

GPT-4 marks a major leap in OpenAI’s deep learning journey, representing a milestone in model scaling. It’s the first large multimodal model that processes both images and text.

Generation

Generation refers to how a generative model produces completely new and original content—like text, images, audio, or video—entirely from scratch using learned data patterns.