How is an AI Agent different from a chatbot?
Chatbots mostly reply in conversation. Agents can call queries, create drafts, or run flows under rules—but enterprise scenarios should still limit tools and permissions.
AI Agent
Agents are one of our services—not the site’s only pitch. We build workflows that query designated data, call allowed tools, and pause for human confirmation on critical steps.
The model only proposes the next recommended step.
Query data or draft content through allowed functions.
When permissions are insufficient or risk is high, switch to human confirmation and write an audit log.
時程:A first Agent on existing APIs / knowledge bases commonly takes 4–12 weeks.
費用:Scoped by number of tools, permissions, and whether a knowledge base is included—can start with a PoC.
正式價格於需求確認後提供,以上為參考。
Leaf AI Workspace:掃描 NAS 資料夾做企業文件知識庫、附頁碼回答,並用受控工具查專案/帳款,通知前先人工確認。
說明企業 AI Agent 與 Chatbot 的差別,以及為什麼重要操作不應交給模型自行決定。
說明 RAG 如何把公司 PDF、SOP 與 FAQ 變成可提問的知識庫,以及它和直接使用 ChatGPT 的差別。
Chatbots mostly reply in conversation. Agents can call queries, create drafts, or run flows under rules—but enterprise scenarios should still limit tools and permissions.
RAG retrieves documents and answers with citations. An Agent decides whether to call an allowed tool, such as looking up an order or drafting a notice. Enterprise assistants often combine both: policy questions use RAG, operational lookups use controlled tools, and email or database writes wait for a person.
Not as a first version. Lookups can be live. Payments, deletes, outbound messages, and email should become drafts first, then an audit record after a person confirms.
Share your current workflow, existing systems, or the problem you want to solve. We first check technical fit, then decide on a PoC or a formal quote.
Free development assessment