Agentic AI Definitions to Know and Understand

Agentic AI represents one of the most exciting frontiers in artificial intelligence, promising to transform how businesses operate by creating systems that can work more autonomously and proactively than ever before. Understanding this technology has become increasingly important for forward-thinking business professionals, but some of the terminology remains vague for those just getting started. This glossary will equip you with the essential terminology and concepts to navigate the evolving landscape of agentic and generative AI.

Agentic AI

AI systems designed to autonomously pursue complex goals and workflows with limited direct human supervision. Unlike traditional AI that follows rigid rules or merely responds to prompts, agentic AI can independently take actions, adapt in real-time, and make decisions based on context and objectives.

AI Agents

Software programs or systems designed to perceive their environment, make decisions, and take actions to achieve specific goals autonomously. In simple terms, think of an AI agent as working almost like a junior colleague: it receives a goal, analyzes incoming data, and makes decisions based on that data.

AI Ethics

A set of moral principles and practices intended to guide companies through the responsible use and development of artificial intelligence. While agentic AI has become more powerful, it raises important concerns around safety, fairness, accountability, and control.  Ethical AI should aim to prevent harmful outcomes, and the AI systems should align with human values that promote equitable, trustworthy interaction with people.

Artificial Intelligence (AI)

A broad concept encompassing machines performing tasks that typically require human intelligence, including problem-solving, perception, and reasoning. AI serves as the umbrella term for various specialized applications and approaches that simulate intelligent behavior.

Autonomy

The ability of an AI system to operate independently, making decisions and taking actions without constant human oversight. This represents a shift from reactive systems that merely respond to prompts toward proactive systems that can initiate actions based on goals.

Conversational AI

AI systems designed to simulate conversation with a human user via text or voice, often implemented as chatbots for customer service, FAQ responses, or other interactions.

Deep Learning

A subset of AI and machine learning that uses artificial neural networks to enable digital systems to learn and make decisions based on either structured or unstructured data. Deep learning mimics the brain's neural networks to process information in multiple layers, allowing computers to recognize patterns and solve complex problems.

Diffusion

A powerful technique for generating high-quality synthetic data by adding and then reversing noise in a controlled manner. This process is essential for creating realistic images, videos, and other forms of synthetic data. This is in contrast to auto-regressive models (ARMs), that generate text sequentially, token by token, where each token is predicted based on the preceding tokens.

Emergent Behavior

Complex patterns or behaviors that arise from the interaction of simple components in an AI system, without being explicitly programmed. Much like ants in nature, a single ant on its own can only perform simple tasks such as wandering, reacting to basic signals, and carrying out routine actions. But when it rejoins the colony, something powerful happens. Together, the ants can explore complex environments, solve problems, and adapt to constant change. There is no central leader and no single ant is in charge, but through their interactions, the colony as a whole becomes remarkably intelligent and effective. This phenomenon is observed in neural networks, multi-agent systems, and other complex AI structures.

Generative AI

Artificial intelligence systems that can create original content—such as text, images, video, audio, or software code—in response to a user's prompt or request. Generative AI, often shortened to GenAI, leverages deep learning models to identify patterns in huge datasets and uses this information to generate new, relevant content that wasn't explicitly programmed and can reuse what it learns to solve new problems

Guardrails

Protective structures designed to prevent AI systems from causing harm, making mistakes, or diverging from ethical standards. These include frameworks, tools, and governance measures to ensure AI operates ethically, safely, and reliably within defined boundaries.

Hallucination

Instances when an AI system can produce  information that appears credible but is, in fact, incorrect, misleading, or entirely made up. This can occur in various AI applications, including language models, image generation tools, and autonomous vehicles.

Large Language Models (LLMs)

Large Language Models (LLMs) are AI systems trained on massive amounts of text data that can process, understand, and generate human language. As a specialized subset of machine learning known as deep learning, LLMs use complex neural networks to recognize patterns in language. It’s capable of understanding relationships between words, concepts, and context, and generating relevant, context-appropriate content. Think of LLMs as the linguistic powerhouses that enable many of today's most impressive AI applications, from chatbots to content creation tools.

Machine Learning

Machine learning is a branch of artificial intelligence where computers learn from data and recognize patterns, can make predictions, or solve problems, without the need to be explicitly programmed to do so. Instead of ML’s following hard-coded rules, the system improves over time as it’s exposed to more information, much like how humans learn from experience.

Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard designed to enable seamless interaction between AI-powered applications and external data sources. Developed by Anthropic and introduced in late 2024, MCP aims to simplify the process of connecting AI models to diverse contexts, making it easier for organizations to build comprehensive AI systems that can work across multiple data sources and tools in a standardized way.

Reinforcement Learning

A subset of ML that uses technologies that allow AI to improve through trial and error and feedback loops. They work toward reinforcing your goal, and anything detracting from such goal is ignored. Think of optimization goals for a marketing campaign or even marketing personalization.

Natural Language Processing (NLP)

Facilitates communication and understanding of user input, allowing agentic AI systems to interpret human language and respond appropriately.

Neural Network

A type of machine learning, it’s inspired by deep learning, and is very similar to how the human brain is structured. It uses interconnected nodes (neurons) that can process and transmit information.

Prompt

Instructions given to an AI system by the user or internally by the system itself, like a chatbot, to generate a specific output or perform a particular task. A prompt can also be known as an input.

Structured Data

Information that is organized in a predefined format, such as databases or spreadsheets, making it easily searchable and analyzable by computers.

Test Time Compute

The amount of computational power and time used by an AI model when it generates a response or performs a task after being trained. This concept is crucial for enhancing the reasoning capabilities of large language models (LLMs) and other AI systems, allowing them to handle complex tasks more effectively. Research indicates that scaling test-time compute can be more effective than simply increasing model size or training data volume for solving complex reasoning tasks.

Tool Integration

The ability to connect with APIs, software, and databases for task execution, enabling agentic AI to interact with and leverage existing digital infrastructure.

Unstructured Data

Information that doesn't have a predefined data model or organization, such as text documents, images, or audio files. Deep learning is particularly effective at processing this type of data.

As you begin integrating agentic AI into your business processes, this glossary should serve as a reference point for understanding both the terminology and the underlying concepts that make this technology so powerful. The journey from traditional AI to truly agentic systems is just beginning, and staying informed about these developments will be crucial for maintaining competitive advantage in an increasingly AI-driven business landscape.