Term explanation
Written communication is the transmission of information through written text — including emails, reports, proposals, presentations, messages, and documentation. Unlike spoken communication, written messages persist over time and can be reviewed, shared, and referenced repeatedly. Effective written communication requires clarity, appropriate structure, careful word choice, and an understanding of the reader's needs and context. In business settings, it is one of the primary channels for formal decisions, instructions, and record-keeping.
LIZ AI bridges written and visual communication. It transforms written reports, data, and briefings into structured, brand-compliant slide decks — automatically — so the written work translates directly into compelling presentations.
Learn more →Formal communication follows established channels, structures, and protocols within an organization or institution. It includes official announcements, board reports, written policies, structured presentations, and any message delivered through authorized pathways. Formal communication is carefully worded, documented, and often subject to approval or review processes. It ensures accountability and consistency, particularly in regulatory, legal, or governance contexts where clear records are essential.
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 moreAgent memory refers to an AI agent's ability to retain and recall information across tasks and sessions. Two types are commonly distinguished: short-term memory, which holds context within a single agent loop interaction, and long-term memory, which persists across sessions and stores facts, preferences, and historical decisions. Memory is what transforms a stateless AI tool into a context-aware agent that produces increasingly relevant results over time — a core requirement for production Agentic AI deployments.
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