AI terms, explained without the hype.
Clear, accurate definitions of the concepts behind agentic AI and AI automation — written so a non-technical leader can follow them.
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Definitions
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Each entry answers the question directly, then explains how it relates to building real systems.
Agentic AI
Agentic AI is AI that can pursue a goal across multiple steps on its own — deciding what to do next, calling tools or APIs, and adapting based on results — rather than producing a single response to a single prompt. It plans, acts, observes, and repeats until the task is done.
Read definition →AI Agent
An AI agent is a software component, usually built on a large language model, that takes a goal and works toward it by reasoning, calling tools, and acting in a loop. Unlike a chatbot that just replies, an agent can fetch data, update systems, and decide its own next step.
Read definition →Agentic Workflows
Agentic workflows are business processes where one or more AI agents carry out multi-step work — reading inputs, making decisions, calling tools, and handing off to people — instead of a fixed, hard-coded sequence. They combine model reasoning with system actions to complete real tasks end to end.
Read definition →Agentic Orchestration
Agentic orchestration is the coordination of multiple AI agents and tools so they work together on a larger task — routing work between agents, managing shared context and state, sequencing steps, handling errors, and inserting human checkpoints. It is the control layer that makes multi-agent systems reliable in production.
Read definition →AI Workflow Automation
AI workflow automation uses AI — often large language models and agents — to carry out repetitive, multi-step business processes such as support, operations, finance, onboarding, and reporting. It handles messy, language-heavy work that rigid rule-based automation cannot, while keeping human checks on important decisions.
Read definition →Human-in-the-Loop (HITL)
Human-in-the-loop is a design approach where a person reviews, approves, or corrects an AI system's output at defined points before it takes effect. It keeps accountability and judgement with people on high-stakes steps, while the AI handles volume — improving reliability and giving teams control over outcomes.
Read definition →Large Language Model (LLM)
A large language model is an AI model trained on very large amounts of text to predict and generate language. It can read, summarise, classify, translate, and draft text, and can power chatbots and AI agents. LLMs are flexible but can make confident mistakes, so outputs need verification.
Read definition →Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that grounds a large language model in your own data. Before answering, the system retrieves relevant documents from a knowledge base and gives them to the model as context, so responses reflect your specific, current information instead of only the model's training data.
Read definition →Vibe Coding
Vibe coding is building software by describing what you want in natural language and letting an AI tool generate the code, rather than writing it line by line. It speeds up prototyping and lets non-specialists create working software, but production use still needs review, testing, and engineering rigour.
Read definition →AI Software House
An AI software house is a software delivery company that designs, builds, and runs AI-powered systems — agents, workflow automation, and custom tools — as well as conventional software. It combines product and engineering skills with applied AI, taking projects from scoping through to a maintained system in production.
Read definition →White-Label AI Delivery
White-label AI delivery is when a specialist partner builds AI solutions — agents, automation, custom tools — that another company sells and delivers under its own brand. The agency keeps the client relationship; the partner provides the AI engineering capacity behind the scenes, letting firms offer AI services without building an in-house team.
Read definition →Answer Engine Optimization (AEO)
Answer engine optimization is the practice of structuring content so AI answer engines — like ChatGPT, Perplexity, and Google's AI overviews — can find, understand, and cite it. It focuses on clear, accurate, self-contained answers and structured data, so your information is surfaced when AI responds to a question.
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