Term explanation
A multi-agent system is a setup in which several autonomous AI agents work together, each handling a specific part of a larger task. The agents can communicate, divide work, and combine their outputs to achieve goals that would be difficult for a single model. Typically, an orchestrator agent coordinates the workflow while specialist agents execute defined subtasks. In enterprise contexts, multi-agent systems allow complex workflows — such as researching a topic, drafting content, checking compliance, and distributing a presentation — to be fully automated.
The architecture behind LIZ AI is built on multi-agent principles: specialized agents handle data retrieval, content composition, brand compliance, and distribution — working in concert to deliver complete, production-ready presentations.
Learn more →The 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.
Learn moreA personal response system (PRS) — also called an audience response system or clicker system — allows individual participants to respond to questions or vote in polls during a presentation or class. Each participant uses a handheld device or smartphone to submit their answer, and results are aggregated and displayed instantly. PRS technology is used in lectures, corporate training, and conferences to increase participation, gauge understanding, and make sessions more interactive.
Learn moreA multi-agent system is a setup in which several autonomous AI agents work together, each handling a specific part of a larger task. The agents can communicate, divide work, and combine their outputs to achieve goals that would be difficult for a single model. Typically, an orchestrator agent coordinates the workflow while specialist agents execute defined subtasks. In enterprise contexts, multi-agent systems allow complex workflows — such as researching a topic, drafting content, checking compliance, and distributing a presentation — to be fully automated.
Learn moreB2C events (business-to-consumer events) are organized experiences designed to engage end consumers directly — such as product launches, brand activations, pop-up experiences, festivals, or public demonstrations. Unlike B2B events, B2C events prioritize emotional connection, entertainment, and brand perception over formal knowledge exchange. They are used to build brand awareness, drive purchase consideration, and create memorable experiences that consumers associate with a product or brand.
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