Artificial intelligence is rewriting the world. Simultaneously, it is inventing a whole new language to describe how it’s doing it. Sit in on any product meeting or industry panel, and you will hear people toss around terms like LLMs, RAG, and RLHF. It is enough to make even the smartest people in tech feel insecure.
This glossary is our attempt to fix that. These are the plain-English definitions of the AI terms you are most likely to encounter, whether you are building, investing, or just trying to keep up. We update this list regularly as the field evolves. Consider it a living document.
Quick Answer: This glossary provides clear, actionable definitions for the most critical AI terminology in 2026, helping you navigate the rapid evolution of large language models, autonomous agents, and the infrastructure that powers them.
The Big Concepts: AGI and Agents
AGI (Artificial General Intelligence): This is a nebulous term. It generally refers to AI that is more capable than the average human at most cognitive tasks. OpenAI’s charter defines it as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind views it as AI that matches human capability across most cognitive domains. Experts still argue over the exact threshold. They likely always will.
AI Agent: An AI agent is a tool that performs a series of tasks on your behalf. It goes beyond a basic chatbot. It can file expenses, book travel, or manage software workflows. The field is still maturing. Infrastructure is catching up to the vision. At its core, an agent is an autonomous system that draws on multiple models to execute multi-step goals.
How Models Actually Think
Chain of Thought: Humans use pen and paper to solve complex math. We break problems into steps. In an AI context, chain-of-thought reasoning means forcing a model to break down a problem into smaller, intermediate steps. It takes longer. But the results are more accurate. This is the foundation of modern reasoning models.
Deep Learning: This is a subset of machine learning. It uses multi-layered, artificial neural networks to identify patterns. Think of it as the engine room of modern AI. It allows systems to learn from vast amounts of data without explicit human programming for every scenario.
The Infrastructure of Intelligence
Compute: This is the vital computational power that allows models to operate. It is the fuel of the industry. The term is often shorthand for the hardware involved—GPUs, CPUs, and TPUs. Without compute, there is no AI. It is the bedrock of the entire sector.
API Endpoints: Think of these as buttons on the back of software. Developers use them to build integrations. They allow one application to pull data from another. As AI agents grow, they are increasingly finding and pressing these buttons themselves. This creates massive potential for automation.
Specialized AI Roles
Coding Agents: These are specialized AI agents applied to software development. They do not just suggest code snippets. They write, test, and debug entire codebases autonomously. They handle the iterative, trial-and-error work that consumes a developer’s day. Think of them as a fast intern who never sleeps. You still need to review the work.
Frequently Asked Questions
What is the difference between a chatbot and an AI agent?
A chatbot is designed to converse and provide information. An AI agent is designed to take action. While a chatbot might tell you how to book a flight, an agent can navigate the website, select the seat, and process the payment for you.
Why is 'compute' such a big deal in AI news?
Compute is the physical constraint of the industry. Because training advanced models requires thousands of specialized chips working in unison, the availability and cost of compute determine which companies can compete at the frontier of AI development.
Does 'chain of thought' make AI smarter?
It makes AI more reliable. By forcing the model to show its work, the system is less likely to hallucinate or skip logical steps. It is a technique for improving accuracy rather than increasing raw intelligence.
Key Takeaways
- AGI remains a moving target: There is no industry-wide consensus on when we will reach it or exactly what it looks like.
- Agents are the next frontier: The shift from passive chatbots to active agents that can use API endpoints is the most significant trend in current AI development.
- Compute is the bottleneck: The physical infrastructure required to train and run these models remains the primary driver of industry economics.
What to Watch Next
The next major shift will occur in the third quarter of 2026, when the next generation of reasoning models is expected to hit enterprise markets. Watch for how these models handle multi-step, high-stakes tasks without human intervention. That is the real test of the agentic era.
