Agent Loop

Agent Loop

Term explanation

Definition and meaning

The agent loop is the core operating cycle of an autonomous AI agent. It runs continuously through four phases: Perception (gathering information), Reasoning (planning the next step), Action (executing — such as calling a tool or generating content), and Observation (evaluating the result). The loop repeats until the task is complete or the agent requires human input. This is the mechanism behind Agentic AI systems — it is what allows agents to handle complex, multi-step tasks that a single prompt-and-response model could not.

LIZ AI's core runs on an agent loop: it continuously perceives changes in your connected data systems, plans the appropriate presentation updates, executes them in PowerPoint, and verifies the result — automatically and repeatedly.

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Other glossary terms

Data-Driven Presentation

A data-driven presentation is a slide deck in which the content — charts, KPIs, tables, and narrative text — is directly derived from live or structured data sources rather than manually entered. Rather than copying figures from a dashboard into PowerPoint, data-driven presentations pull information automatically from connected systems such as CRM, ERP, or BI tools. The result is a living presentation that always reflects current data — and is the foundation of Agentic Slides architecture.

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Persuasive Presentations

A persuasive presentation is designed to change the audience's opinion, attitude, or behavior. The presenter builds a case using evidence, logic, and emotional appeal to move the audience toward a specific conclusion or action. Persuasive presentations are common in sales pitches, political speeches, fundraising campaigns, and change management initiatives. They differ from informative presentations in that they take a deliberate position and actively seek buy-in.

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Agentic AI

Agentic AI refers to artificial intelligence systems that act autonomously to achieve multi-step goals — without requiring a human to trigger each action individually. Unlike traditional AI that responds to single prompts, agentic AI plans, decides, and executes sequences of tasks on its own, often integrating with external tools and data sources. In enterprise settings, agentic AI is increasingly used to automate complex workflows such as reporting, content creation, and communication. In the domain of presentations, this approach is realised through the Large Presentation Model (LPM) — an agentic AI system that orchestrates the entire presentation cycle in an enterprise context.

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Asynchronous Learning

Asynchronous learning refers to educational experiences that do not require all participants to be present at the same time. Learners access materials, complete exercises, and submit work according to their own schedule within a defined timeframe. Common formats include recorded video lectures, discussion boards, and self-paced e-courses. Asynchronous learning offers flexibility for geographically dispersed or busy learners and forms the backbone of most online learning programs.

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