Term explanation
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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Learn more →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.
Learn moreAn AI-powered workflow is a business process in which artificial intelligence automates one or more steps that would otherwise require manual work. This can range from simple rule-based automation to fully autonomous agents that plan, execute, and adapt in real time. In communication and marketing teams, AI-powered workflows are used to streamline content production, approval processes, and distribution — reducing time-to-delivery and freeing teams for higher-value work.
Learn moreHuman-in-the-loop (HITL) refers to a design pattern in AI systems where a human is involved at specific decision points to review, approve, or correct the AI's actions before they are executed. Rather than running fully autonomously, the system pauses at predefined checkpoints and waits for human confirmation — particularly for high-stakes or irreversible actions. HITL works alongside AI guardrails as a key governance principle in enterprise Agentic AI, balancing the efficiency of automation with accountability and human judgment.
Learn moreThe Model Context Protocol (MCP) is an open standard developed by Anthropic in 2024 and widely adopted in 2025 by OpenAI, Google, and Microsoft. It defines a standardized way for AI agents to connect to external tools, data sources, and enterprise systems — without requiring custom integrations for every connection. MCP acts as a universal interface: an AI agent with MCP support can securely access databases, APIs, document repositories, and business applications using a consistent protocol, regardless of the underlying system. This dramatically simplifies how AI is embedded into complex enterprise environments.
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