Glossary

The vocabulary of agentic go-to-market, defined in one sentence each. Where a term is used to mean two different things, the entry says which one is which.

Agentic State: Agentic state is the data an agent carries between steps — goal, message history, tool results, progress markers — stored outside the model so a run can resume, be audited, or be capped.
AI Agent: An AI agent is a software system that perceives its environment, chooses its own next action toward a goal, and acts through tools, repeating that loop until a stop condition is met.
AI Agent Memory: AI agent memory is the machinery outside the model that stores facts from one run and reads selected ones back into the prompt on later runs, so behaviour persists across sessions.
AI moat: An AI moat is the part of an AI product's advantage that still stands when a competitor builds the same thing on the same model.
AI SaaS: AI SaaS is cloud-delivered software in which a machine-learning model performs the product's core function, which makes the vendor's cost of goods rise with usage instead of staying near zero.
Autonomous agent: An autonomous agent is a software system that pursues a goal by choosing its own next action — usually a tool call — at each step, rather than following a path fixed in code.
Computer Use Agent: An AI system that operates a computer the way a person does — reading the screen as an image and issuing mouse and keyboard actions — instead of calling an API.
Multi-Agent System: A multi-agent system is two or more autonomous agents, each with its own state and instructions, that interact by messages or handoffs to complete a task none of them handles end to end.