Introduction to Agentic AI
Agentic AI is AI that pursues a goal on its own, planning steps, using tools and checking results. Learn how it works, real examples and the risks.
Agentic AI is artificial intelligence that pursues a goal on its own: it breaks the goal into steps, uses tools such as search, databases and APIs to carry them out, checks the results, and adjusts until the task is done. Where a standard AI model answers a single prompt, an agentic system decides what to do next, usually with a large language model (LLM) as its reasoning engine.
Agents are not a new concept in the field of artificial intelligence (AI). They have been around for decades, but recent advancements in AI technology have made them more powerful and capable than ever before. In this article, we will explore the concept of agentic AI, its capabilities, and its applications in various fields.
What is Agentic AI?
Agents, by definition, are autonomous entities that can perceive their environment, reason about it, and take actions to achieve specific goals. They can be physical robots or virtual agents that operate in digital environments. Agentic AI refers to AI systems that possess the ability to act autonomously and make decisions based on their understanding of the world.
The key point about agentic AI is that it can operate independently, using a proactive approach with a goal-based mindset. This means that agents can analyze their environment (perceive), understand the context (reason), and take actions to achieve their objectives (act). Agentic AI systems can autonomously plan the steps and make decisions to achieve their given goals based on its understanding of the environment.
This is in contrast to traditional AI usage, which often takes a straightforward approach to problem-solving, solving the problem at hand as one big problem rather than breaking it down into smaller, manageable tasks. Traditional AI systems are often reactive, responding to specific inputs or queries without the ability to take initiative.
Refresher: What is an LLM?
A large language model (LLM) is a type of AI model that has been trained on vast amounts of text data to understand and generate human-like language. LLMs are capable of performing various natural language processing tasks, such as text generation, translation, summarization, and question-answering. Their main strength lies in their ability to understand context and generate coherent and contextually relevant responses, but they primarily responds based on input context and do not have the ability to act autonomously or set their own goals.
We can think of LLMs as a powerful tool that can be used by agentic AI systems to enhance their capabilities. Sort of like a brain that can process and understand language, but lacks the ability to act on its own. By making LLMs into the brain of agentic AI systems, we are equipping the intelligent brain with arms and legs to act on its own.
Agentic AI vs Generative AI
Generative AI produces content, such as text, images or code, in response to a prompt, and the user decides each next step. Agentic AI uses a generative model as its reasoning engine but adds a goal, planning, tools and memory, so the system decides the steps itself and acts on other systems until the task is done. A chatbot that only answers questions is generative but not agentic; an agent that reads a ticket, checks the order system and updates the customer record is both.
Anatomy of an AI Agent
An AI agent is typically composed of several key components that work together to enable autonomous decision-making and action-taking. These components include:
- Goal: The objective or task that the agent is trying to achieve. This can be a specific action, such as navigating to a location, or a more complex goal, such as optimizing a process.
- Environment: The context in which the agent operates. This can include physical spaces, digital environments, or social contexts. The environment provides the agent with information and feedback about its actions and decisions.
- Perception: The ability of the agent to sense and understand its environment. This can take form in various ways, such as visual perception, or even text-based perception, like logs, emails, documents, and other forms of data.
- Reasoning: The process of understanding the information and make inferences based on the agent's knowledge and experience.
- Planning: The process of determining the best course of action sequence to achieve the agent's goals. This involves evaluating different options and selecting the most appropriate one based on the agent's understanding of the environment.
- Action & Tool Use: The physical or virtual actions taken by the agent to achieve its goals. This is where agents differ from passive models. Actions often involve Tool Use, which is the ability to interact with external systems like APIs, databases, search engines, or other applications. This allows the agent to affect its environment and gather information beyond the internal knowledge it possesses.
- Memory: The ability to store and retrieve information about past experiences, knowledge, and actions. This is crucial for learning and improving performance over time.
- Learning: The ability of the agent to adapt and improve its performance over time. This can involve updating its knowledge base in its memory, refining its decision-making processes, and learning from past experiences.
- Communication: The ability of the agent to interact with other agents or humans. This can involve sharing information, coordinating actions, and collaborating to achieve common goals.
Why Is Agentic AI Important?
Agentic AI marks a shift towards how we interact with AI. Instead of asking questions and receiving answers, we can now give AI systems goals and let them autonomously figure out how to achieve those goals. This opens up a world of possibilities for automating complex tasks, optimizing processes, and enhancing decision-making across various domains.
Some of the things enabled by agentic AI include:
- Automation: Agentic AI can automate complex tasks that require multiple steps and decision-making processes. This can lead to increased efficiency while reducing human intervention, error, and effort.
- Personalization: Agentic AI don't just respond. It proactively learns and acts based on the given context, allowing for more personalized experiences and interactions.
- Problem Solving: Agentic AI can tackle complex problems by breaking them down into smaller tasks and finding optimal solutions. It combines the power of LLM reasoning with actions (tools) and can collaborate with other agents to solve problems more effectively.
In the next part of this series, Building Your First Agent, we put these components together into a working agent. If you are planning agents for your organization, see how Supertype builds custom AI agents for companies.
Frequently Asked Questions
What is the difference between agentic AI and generative AI?
Generative AI produces content such as text, images or code in response to a prompt. Agentic AI uses a generative model as its reasoning engine but adds goals, planning, tools and memory, so it can carry out a multi-step task and act on other systems instead of only returning an answer.
Is agentic AI the same as a chatbot?
No. A chatbot answers questions within a conversation. An agentic system is given a goal and decides which steps to take, which tools to call and when the job is done. A chatbot can be the front end of an agent, but answering messages alone does not make a system agentic.
What is the difference between agentic AI and an AI agent?
An AI agent is a single system that perceives, reasons and acts toward a goal. Agentic AI is the broader approach of building software around such agents, including setups where several agents, tools and humans work together on one workflow.
What are examples of agentic AI in business?
Common examples include agents that triage and resolve support tickets, reconcile invoices against purchase orders, prepare recurring reports from several data sources, research and summarize documents, and move records between systems such as an ERP, CRM or ticketing tool, with a human approving the sensitive steps.
What are the main risks of agentic AI?
Agents can take wrong actions with real consequences, misuse tools, leak data they can access, loop or run up costs, and are harder to audit than a single model call. Teams manage this with narrow permissions, human-in-the-loop approvals, logs and tracing of every step, and evaluation before and after deployment.
How should a company get started with agentic AI?
Start with one well-defined, repetitive workflow that has clear inputs, a measurable outcome and a human who can review results. Give the agent only the tools it needs, log every step, keep a person in the loop for irreversible actions, and expand scope once it performs reliably.
Like this piece? Take home Blueprints from Agentic Patterns.
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