What Is Agentic AI?

In this article, you'll learn:

• what is the definition of Agentic AI
• how Agentic AI is a critical path toward artificial general intelligence
• what Agentic AI systems already exist
• the differences between AI agents and Agentic AI
• what are some examples of AI agents and Agentic AI systems

Agentic AI represents a significant shift in the evolution of AI. Its capabilities are leaps and bounds beyond those of Generative AI (GenAI) and other traditional forms of AI, such as voice assistants like Siri and Alexa, or the technologies that drive autonomous cars. The most distinguishing feature that puts Agentic AI in a league of its own is autonomy (its capacity to make decisions and carry out a set of actions toward a goal without specific instruction at each step).

Put in a simpler way, GenAI is all talk, and Agentic AI is all action.

Agentic AI definition

Agentic AI is a type of artificial intelligence that can act on its own to achieve goals, instead of just waiting for prompts from a human. It doesn’t just respond to a human’s commands at every step. It can decide what steps to take, make plans, change its approach if something doesn’t work, keep track of what it learns along the way, and reflect on its performance so that it can improve.

The word agentic comes from agent, meaning that the AI behaves like an agent on your behalf, an intelligent helper, or a problem-solver that has a degree of independence. Think of Agentic AI as AI that not only completes tasks but also figures out how to complete them.

This isn’t the AI of scary science fiction stories. However, technology experts widely view Agentic AI as a critical stepping stone on the path toward artificial general intelligence (AGI; the form of AI depicted in scary science fiction stories) and possibly the technological singularity — or simply the singularity — a hypothetical future point at which artificial intelligence surpasses human intelligence in a way that leads to unpredictable, rapid, and irreversible changes in society and technology.

Moving toward artifical general intelligence with Agentic AI

By enabling systems to reason, plan, reflect, and take initiative across changing environments, Agentic AI helps bridge the gap between today’s highly specialized models and the broad, self-directed intelligence that systems need to realize AGI.

In the broader vision of the hypothetical singularity, exponentially advancing artificial intelligence could emerge through self-improving, interconnected agentic systems. Such systems might continually refine their own architectures and methods, collaborate with other agents (even across different networks or domains), and pursue complex goals with diminishing need for direct human oversight.

However, Agentic AI and more autonomous systems bring us a step closer to the kind of self-directed intelligence imagined in singularity scenarios, and they also introduce new risks of unintended consequences and demand additional safeguards, including

• Strong alignment with human values, training and guiding models by using data, feedback, and objectives that reflect humanity’s shared ethical and social principles.

• Robust guardrails that provide clear, operational boundaries and fail-safes that define what the AI can and cannot do, even as it learns or acts independently.

• Ethical oversight to maintain human accountability in how developers design, deploy, and monitor agentic systems throughout their lifecycle.

What agentic systems already exist?

Developers often design Agentic AI to handle complex, multi-step processes, such as managing projects, conducting research, or solving technical problems. These systems can include tools such as memory (to track what’s already been done), reasoning engines (to decide what makes the most sense), and planning modules (to map out steps and sequences). Although these systems aren’t really humanlike or conscious, they’re moving closer to becoming trusted aides to the people using them.

What are the differences between AI agents and Agentic AI?

The two terms AI agents and Agentic AI systems are often used interchangeably in discussions about artificial intelligence, but they actually describe different concepts that share overlapping features. Understanding their differences can help clarify the different types of AI that exist today and where they might be headed as they evolve toward more capable and autonomous systems:

• AI agents: Software entities designed to perform specific tasks autonomously within defined parameters. They operate based on programmed rules (rule-based agents), machine learning models (learning-based agents), or large language models (LLM-based agents) to achieve particular objectives by perceiving their environment, making decisions, and taking actions. Examples include customer-service chatbots, game-playing bots, web navigation agents, and recommendation systems.

• Agentic AI systems: Also software entities, but they extend basic task automation into the realm of complex, multi-step process management. These systems can plan their own actions, coordinate multiple tools or agents, and adapt their workflows to reach broader objectives, often across less predictable environments.

What are examples of AI agents and Agentic AI systems?

Examples of AI agents include chatbots such as ChatGPT, recommendation systems that suggest what to watch or buy, or simple robotic agents, such as a Roomba vacuum cleaner. These agents typically have a narrow scope and focus on well-defined tasks with limited adaptability to new contexts.

An Agentic AI system goes further than a chatbot such as ChatGPT. Agentic systems often combine multiple AI agents and add capabilities such as goal-setting, planning, reasoning, and task monitoring over extended periods. One example is Godmode (www.godmode.space), an online interface that lets you launch and manage autonomous AI agents such as AutoGPT (www.agpt.co) and BabyAGI (www.babyagi.org). Godmode doesn’t crowdsource from these projects; instead, it provides a user-friendly control panel that connects to and coordinates open-source agent frameworks behind the scenes.