Should Every Business Build AI Agents?
**Dan's test for an agent**
Dan recommends using the Rule of R before building:
- Repetitive: the work happens regularly.
- Rules-based: the process and expected result can be explained.
- Return on time: the saved effort justifies the build and maintenance.
This test matters because AI makes overbuilding easy. A founder can spend days automating a task that previously required a few minutes.
**When an agent may be useful**
- Preparing a recurring private report
- Organising incoming information
- Drafting responses for approval
- Monitoring a defined metric
- Running a repeatable research process
- Coordinating low-risk internal workflows
**When an agent may be inappropriate**
A task may not deserve an agent when it is rare, highly ambiguous, sensitive, or inexpensive to complete manually.
Businesses should also avoid giving immediate autonomy to an untested system. Dan recommends increasing trust in stages. The agent first shows what it would do, then prepares drafts, then completes limited actions after proving reliability.
**The hidden work**
An effective agent still requires process documentation, context, permissions, error handling, monitoring, and maintenance. The agent may reduce operational work while creating a new responsibility for system supervision.
**Community question**
**Which recurring task in your work genuinely deserves an AI agent, and which task would be faster or safer to keep manual?**
Describe the task, how often it happens, the cost of an error, and how you would measure whether the agent saved real time.
*This is independent WhatAI editorial coverage. Dan Martell has not endorsed or sponsored this post.*