Qlik for Agentic Analytics

An AI agent is a software system that uses artificial intelligence (often a large language model) to autonomously pursue a goal by perceiving its environment or data, reasoning about what to do, and taking action across multiple steps. Unlike a passive chatbot that just responds to each prompt, an agent can plan, use tools, call other systems, and adjust based on results, all with limited human intervention. Agents are the building blocks of more advanced, automated AI workflows. 

A large language model (LLM) is a type of AI model trained on vast amounts of text to understand and generate human language. It works by predicting likely sequences of words, which lets it answer questions, summarize, translate, write, and reason about text. LLMs (like those behind popular AI assistants) are the technology that powers modern chatbots, copilots, and generative AI applications.

Generative AI is a category of artificial intelligence that creates new content (text, images, code, audio, or video) from patterns learned in training data, in response to a prompt. It differs from traditional AI that mainly classifies or predicts, because it produces original outputs rather than just analyzing existing data. Large language models are a form of generative AI focused on language.

Retrieval-augmented generation (RAG) is an AI technique that improves a large language model's responses by retrieving relevant information from external knowledge sources and giving it to the model before it generates an answer. This grounds the output in accurate, current, and domain-specific facts, rather than relying only on what the model learned in training. RAG is widely used to cut hallucinations and keep AI answers reliable without retraining the underlying model. 

Chain-of-thought reasoning is an approach where an AI model works through a problem step by step, generating intermediate reasoning before it reaches a final answer. Breaking a task into a sequence of logical steps improves accuracy on complex, multi-step problems, compared with producing an answer in one leap. Carrying context from one step to the next also helps the model reason more coherently across a series of related questions. 

A multi-agent system is an architecture where multiple AI agents, each specialized for a particular task, work together toward a larger goal. Instead of relying on a single agent to do everything, the work is divided: one agent might retrieve data, another analyze it, and another take action, often coordinated by an orchestrating layer. This division of labor makes complex, end-to-end workflows more capable, scalable, and reliable. 

An AI copilot is an AI-powered assistant built into software that helps users get work done by suggesting, drafting, answering questions, or automating steps. The name reflects that it assists and augments a human who stays in control, rather than acting fully on its own. Copilots are commonly built into productivity tools, analytics platforms, and development environments to speed up work. 

An AI hallucination is when a model generates output that's false, fabricated, or unsupported by real data but presented confidently as if it were true. It happens because language models predict plausible-sounding text from patterns rather than checking facts. Hallucinations are a well-known limit of large language models, and grounding techniques like retrieval-augmented generation (which anchor responses to trusted source data) commonly reduce them. 

AI governance is the set of frameworks, policies, and controls that keep artificial intelligence developed and used responsibly, safely, and in line with ethical standards and regulations. It addresses concerns like transparency, accountability, bias, security, privacy, and human oversight of AI decisions. As organizations deploy AI more widely (especially autonomous agents), governance provides the guardrails that keep those systems trustworthy and compliant.

Human-in-the-loop (HITL) is an approach where people stay actively involved in an AI system's process: reviewing, validating, correcting, or approving its outputs and actions. It's especially important for high-stakes decisions, where human judgment guards against errors, bias, or unintended consequences. Keeping a human in the loop balances the speed and scale of AI with accuracy, safety, and accountability.