Insights
Plain-English explanations of core AI concepts and terminology.
Traditional automation follows fixed rules; AI agents reason about a goal and decide what to do. Here's how the two actually differ, and when each one is the right fit for a business process.
Read articleA vector database stores numeric representations of content and finds the closest matches by meaning, not exact keywords, making it the retrieval backbone behind most RAG and enterprise knowledge systems.
Read articleRAG (retrieval-augmented generation) grounds an AI model's answers in passages retrieved from your own documents at query time, rather than relying solely on what the model learned during training.
Read articleAgentic AI refers to AI systems, often multiple coordinated agents, capable of planning and executing multi-step work across an organization's systems. For enterprises, that means automation that can span departments and tools rather than a single task.
Read articleChatbots answer questions in conversation; AI agents can take real action in other systems. The distinction matters when deciding what kind of AI project will actually solve your problem.
Read articleAI agents are systems that can reason about a goal, plan the steps to reach it, and take real action by calling tools and APIs, not just answer questions in conversation.
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