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

AI Presentation Maker

An AI presentation maker is a tool that uses artificial intelligence to automatically generate, structure, and design slide decks based on user input — such as a topic, a text document, or a data file. Most AI presentation makers follow a similar process: the AI analyzes the input, builds a logical slide structure, applies a suitable layout and design, and populates the content. Advanced AI presentation makers go beyond one-time generation: they connect to live data sources, adapt decks to different audiences, and keep presentations updated automatically over time.

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Nonverbal Communication

Nonverbal communication encompasses all forms of information conveyed without words — including body language, facial expressions, gestures, eye contact, posture, and tone of voice. Research suggests that a significant portion of interpersonal communication is nonverbal. In presentations, nonverbal cues strongly influence how a message is received: open posture conveys confidence, eye contact builds trust, and a steady voice signals authority. Presenters who align their nonverbal signals with their verbal content are generally perceived as more credible and engaging.

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Corporate Identity Compliance (CI Compliance)

Corporate identity compliance (CI compliance) describes the degree to which communications materials — such as presentations, documents, and marketing assets — adhere to a company's defined brand guidelines. This includes the correct use of colors, typography, logos, imagery, and language. Maintaining CI compliance is a significant challenge in organizations where many employees create their own materials, often without centralized oversight. AI tools are increasingly used to automate compliance checks and corrections at scale.

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Generative AI

Generative AI refers to artificial intelligence systems that create new content — such as text, images, code, or structured data — in response to a prompt or task, rather than simply analyzing or classifying existing information. Powered by large language models and other foundation models, generative AI can write documents, summarize reports, produce slide content, and translate data into natural language. In enterprise settings, it is the core technology behind modern AI assistants, document automation tools, and presentation generators. A presentation-specific evolution of generative AI is the Large Presentation Model (LPM), which combines generative capabilities with enterprise context, brand guidelines, and the full presentation cycle.

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