Handing an entire recurring job to AI requires more than a well-worded question. An AI agent—a model that can use tools, assess intermediate results, and choose its next actions toward a goal—needs a defined assignment. The 4S method sets out four decisions for the person assigning the work: Select, Scope, Supervise, and Sign off.
▲ Sources, boundaries, review, and delivery
This approach fits jobs that follow similar steps and produce a consistent output each day, week, or month. A weekly briefing, for example, might require gathering updates, filtering them, writing a summary, and saving a document. The aim is to delegate that sequence without giving up control over its sources, boundaries, or final quality.
Turn a recurring job into a 4S assignment
The four steps separate what the agent can do from what a person must decide. They also provide a way to test a workflow before putting it on a schedule.
- Select: Choose a useful, repetitive job rather than automating something for novelty. Pick work you understand well enough to judge. If you cannot tell whether the result is accurate or complete, you cannot reliably review the agent’s performance. To find candidates, ask an agent to interview you about the chores you repeat daily, weekly, and monthly.
- Scope: State the desired outcome, acceptable sources, operating limits, delivery location, and definition of done. For a Monday morning briefing on ChatGPT and Claude, that could mean covering only changes from the previous seven days, using official release announcements, and including only updates relevant to your work. Specify whether the finished document belongs in a folder or whether a notification should go to Slack or WhatsApp.
- Supervise: Decide where a person will check the work. In early briefing runs, have the agent present its chosen sources before it writes the report. That checkpoint can reveal missing updates or coverage tilted toward a tool you rarely use. Give specific feedback and adjust the instructions; reduce routine checks only after repeated runs meet your standards. Keep human checkpoints for sensitive work, including client-facing messages and financial operations.
- Sign off: Accept, request revisions to, or reject the finished deliverable. For a briefing, check the requested timeframe, verify that cited links are real and work, and confirm that an announcement has not been presented as a generally available feature. Describe needed corrections precisely before approving the result.
▲ Instructions and connected work tools
Know what the agent needs to do the job
An agent provides the ability to act toward the assigned goal within the tools and permissions it has been given. ChatGPT Work and Claude Cowork are examples of agent options for this kind of work. Desktop applications are preferable to mobile apps or browser tabs for these workflows because they provide more of the system access and tool controls needed for execution. That capability makes permission choices important: connecting a document archive is different from granting access to a password vault or payment details.
A skill is different from an agent. It is a reusable set of task instructions, much like an operating manual. A skill can hold a research procedure, report-formatting rules, or guidance on writing style. In practical terms, a skill file can be a folder of plain-text instructions that a person can inspect and edit. Once a supervised workflow works reliably, its instructions can be packaged as a skill for repeated use, including across ChatGPT and Claude environments.
Connectors and plugins give an agent access to other applications. A connector might let it read reference files in Google Drive or write an output there; a plugin may bundle tools, prompts, and authentication for a service. An API, or application programming interface, lets software exchange data and request actions. Model Context Protocol (MCP) is a standard for presenting tools and data context to AI assistants, building on those underlying software connections. These are the bridges to a service, not the instructions that tell an agent which work is acceptable.
Test first, then decide what to schedule
A recurring workflow should earn its schedule. An agent might turn a recording transcript into a newsletter draft, gather invoices for a monthly bookkeeping sweep, or check a website weekly for search engine optimization (SEO) issues. Each example has a different output and different review needs. A newsletter draft can await editorial approval; a workflow involving money should continue to have a human check-in even after its routine steps become dependable.
For scheduled work, the computer running the agent matters. A personal laptop that sleeps when its lid closes cannot reliably run background tasks. An always-on dedicated device, such as an Apple Mac mini, or a virtual private server—a remote computer kept available for scheduled work—can support a recurring workflow. First decide which services the agent actually needs, then limit its permissions accordingly. Do not connect sensitive credentials merely to make the workflow more convenient.
Start with one bounded job
Choose one repeated task whose result you know how to assess. Write down its outcome, sources, limits, destination, and review checkpoints; then inspect several runs before scheduling it. If the process proves reliable, turn the instructions into a reusable skill. The agent can carry out the steps and connected tools can move information, but a person still decides what access to grant and when the work is ready to use.