AI terms, explained clearly

Agentic AI refers to AI systems that can plan and take multi-step actions toward a goal — using tools, calling APIs and making decisions — rather than just answering a single prompt. Agents perceive, reason, act and adapt with minimal human intervention.

RAG is a technique that grounds an AI model's answers in your own data. Before responding, the system retrieves relevant documents from a knowledge base and feeds them to the model — producing accurate, source-cited answers instead of generic or hallucinated ones.

A Large Language Model is an AI trained on vast amounts of text to understand and generate human language. LLMs power chatbots, agents and copilots — they can summarize, translate, write, classify and reason over natural language.

An AI agent is a software system powered by an LLM that can autonomously complete tasks — understanding a request, using tools and data, and taking actions across your systems. Examples include voice receptionists, support copilots and lead-qualification agents.

AI workflow automation uses AI to run multi-step business processes end to end — combining tools like n8n or Make with LLMs to handle tasks that need understanding, such as reading documents, drafting replies and updating systems automatically.

Fine-tuning is the process of further training a pre-trained AI model on your specific data so it performs better on your tasks, tone and domain. It's one option for customization — often RAG is faster and cheaper for knowledge-grounding.

A vector database stores text and other data as numerical embeddings so an AI can find the most semantically relevant information quickly. It's the retrieval engine behind RAG and knowledge assistants.

AI governance is the framework of policies, controls and oversight that keeps AI systems safe, compliant and accountable. It covers data privacy, security, bias, auditability and alignment with regulations like GDPR and Gulf PDPL.