Notion's Custom AI Agents Reviewed
A hands-on review of Notion’s new Custom Agents feature suggests the tools could represent the future of productivity workflows. The agents can automate research, generate content, and learn from user behavior to anticipate needs. The review highlights the blurring line between a software tool and an autonomous collaborator.
- Notion's Custom Agents launched publicly in late February 2026 and are designed to automate three core tasks: answering repetitive questions from a knowledge base, triaging tickets and to-dos, and generating automatic status updates. - Early testing with the company Ramp demonstrated that a single agent answered 4,000 questions over two weeks, which was estimated to have saved 2,000 hours of employee time. - The feature is part of a larger strategic push called "Notion 3.0," which frames AI agents as a fundamental evolution of the product, comparable to the initial launch or the introduction of databases. - Custom Agents can be triggered by various events, including a set schedule, a new email, a Slack message, or a change within a Notion database. They can then perform actions like creating or editing Notion pages, sending Slack messages, or updating database properties. - Instead of a flat per-seat fee, the new agents will operate on a usage-based model using "Notion Credits." This is a business model shift CEO Ivan Zhao believes could unlock a market ten times the size of traditional SaaS. - The feature is free for Business and Enterprise customers until May 3, 2026, after which it will require the purchase of credits. A dashboard allows administrators to monitor credit usage and set limits to control costs. - Users can select the underlying large language model for their agents, with options including OpenAI's GPT, Anthropic's Claude, and Google's Gemini, or allow Notion to select the best model for a given task automatically. - The custom agents build upon Notion's existing "Q&A" AI feature, which acts like an internal search engine that can answer natural language questions based on information contained within a user's workspace.