7 Must-Know Use Cases For The New Notion Agents

Notion recently introduced a significant update, termed Notion 3.0, featuring advanced AI agents designed for knowledge work. Deploying these tools requires understanding where they generate genuine value versus merely serving as distractions. The focus here is on seven practical, real-life use cases derived from consulting experience, moving beyond simple to-do list additions. One specific application mentioned promises to save a client thousands of hours in the coming months. Furthermore, a bonus technique addresses how agents can handle large data volumes without frequent interruptions.

Many roles involve the routine task of formatting information, such as product announcements or internal news, into a standardized layout. For instance, weekly content roundups require extracting five highlights from LinkedIn posts and structuring them with headings, main paragraphs, quotes, and links suitable for platforms like LinkedIn and Medium. Instead of dedicating several minutes weekly to manual formatting, users can define the required structure once.

This structure definition can itself be created with AI assistance by providing an example and asking the AI to extract the necessary formatting instructions, forming a Standard Operating Procedure (SOP). Once this guideline is established, applying it to new content becomes a simple request to the agent, resulting in copy-paste ready material that utilizes all of Notion's available blocks.

A key application involves converting general, real-world knowledge into structured data within Notion databases, saving substantial manual effort. One client required a database listing every country globally, complete with its corresponding ISO 2 code and flag for each entry. Manually compiling this information could consume several hours, but Notion AI handles this complex data compilation with a single, precise prompt.

This principle extends to personal use cases, such as planning travel itineraries. For an upcoming trip to Tuscany, general knowledge about recommended locations and activities can be fed to the agent. The request asks the agent to plan the initial version of the trip and structure the output into a database, which can then be visualized using Notion's map view feature, delivering a complete plan within minutes.

Basically, whenever one is tasked with taking any real-world knowledge and turning it into structured data in Notion, AI should be utilized rather than performing the work manually.

Notion Agents offer a significant advantage in avoiding redundant data entry across different platforms, a frequent issue in operational settings. For example, scheduling surveys for buildings must occur in Google Calendar for external access, while simultaneously tracking the appointment status within the Notion database for individual buildings.

To ensure AI functions effectively within a Notion workspace, two critical components are necessary: the AC/DC framework and a general master prompt. These elements provide the necessary context and structure for the agents to operate efficiently, as discussed in a recent presentation at the Make with Notion showcase in Munich.

A major shift is occurring away from static dashboards, which require users to manually navigate interfaces to find their responsibilities, toward dynamic reporting using AI. For instance, instead of clicking through an OKR dashboard, a user can simply ask the notion AI to display the OKRs assigned to them and facilitate the necessary updates.

To facilitate smooth adoption, organizations are packaging pre-written, detailed prompts for users. This allows team members to simply copy and paste instructions when they need to execute a process, removing the cognitive load of figuring out the exact command structure required by the AI.

As companies scale, data architecture often requires adjustments. A common scenario involves a single database tracking heterogeneous entities, such as distributors and suppliers, which necessitates adding properties relevant only to one group. This leads to an unruly system. The optimal solution involves creating a new architecture with one parent partner database and two distinct child tables for distributors and suppliers, maintaining a single relational entry while isolating specific properties.

Data migrations that previously consumed days of manual work can now be executed quickly using AI. The prompt must instruct the agent to first analyze the existing structure, identify which properties belong in the new child databases, set up the new structure, and then meticulously migrate every single entry from the source database to the new segmented system.

Data migrations like this used to take one or more work days because there is a lot of property to go through, coordinate which goes where, and then while there is just a manual process of doing it.

When agents are tasked with updating numerous entries, they sometimes pause after processing only ten items or incorrectly report completion. To ensure continuous operation, it is recommended to switch the agent setting from 'Auto' to 'Cloud' mode. Additionally, instructing the agent to write itself a checklist and track progress on a dedicated page significantly increases adherence to the task.

Maintaining a visible checklist page allows users to easily restart the process if the agent bugs out halfway through hundreds of entries. The agent checks its progress against this list, verifies data integrity, and continues working autonomously until all steps are confirmed complete, resulting in a much faster migration than previously possible.

Notion Agents significantly improve the utility of Notion AI meeting notes, which otherwise only produce plain text highlights and action items. By utilizing a standard prompt, users can instruct the agent after every meeting to extract tasks specifically relevant to them and immediately add those items to their designated task database.

But with agent, you can now as well create yourself a simple standard prompt and after every meeting, just ask it to, hey, can you please extract the tasks from this specific meeting that are relevant to me and add them to my task database.

This use case demonstrates immense potential for organizations dealing with external reporting requirements, such as investors, where Notion's native PDF export functionality is insufficient. For a client managing hundreds of buildings, customer success teams spend significant time weekly compiling progress reports. Agents can automate this by referencing an SOP detailing the required report structure, synthesizing both structured data and qualitative text updates.

A major benefit is that Notion Agents operate asynchronously. Once a request is initiated, the user does not need to remain on the page or even within Notion for the process to complete. This allows for parallel work streams, enabling customer success personnel to move on to other tasks while the report is generated automatically based on the existing data structure.

The potential applications for Notion Agents span utilizing existing data to create accessible reports for various audiences, including VCs reporting to limited partners or internal teams receiving regular updates via Slack. Upon deploying these agents in a company or team workspace, establishing correct permissions is paramount. Unauthorized AI changes or access to sensitive information must be prevented to ensure a secure and reliable system for the entire team.