The Agentic Coding Workflow I Use to Ship Small SaaS Products
A reliable agentic coding workflow connects product judgment, implementation and production verification. This is the sequence I use when a focused SaaS product needs to move from an observed problem to a working release.
1. Start with the expensive moment
Most useful small products begin with a repeated moment of waste: paying too much, scrolling too long, rebuilding the same asset or making a decision with poor information. I write that moment down before discussing technology.
The goal is to describe the change in the user’s situation. “Build a dashboard” says nothing about value. “Show whether the current game price is a real historical low” identifies a decision the software can improve.
2. Define the evidence
Before the agent edits anything, I define what will count as done. Evidence may include a route returning the right status, a setting surviving a restart, a private file remaining inaccessible or a screenshot showing that the interface is readable at a real viewport.
This prevents a common failure: an implementation that looks complete in code but never proves the outcome. The evidence list becomes the final checklist and keeps the agent from optimizing for activity.
3. Inspect the real system
Agentic coding works best when the agent can examine the actual repository, configuration and deployment model. Existing conventions matter. So do shared ports, persistent databases, background processes and files that belong to another product.
Inspection should be targeted. The agent does not need every file. It needs the entry points, persistence layer, relevant UI, deployment mechanism and current production behaviour.
4. Make the smallest complete change
Small does not mean cosmetic. A complete change may cross the interface, server and deployment script, but it should avoid unrelated cleanup. The objective is one coherent outcome with a controlled blast radius.
When possible, I keep new behaviour behind an explicit route or component and reuse existing storage and authentication. That makes the result easier to test and reduces the number of new failure modes.
5. Prove it locally
Local verification checks syntax, data validation, responsive behaviour and the failure path. For server work, I prefer an isolated port rather than touching the running service. For documents, I render every page. For checkout flows, I use sandbox payments.
A test should fail for the right reason. A private PDF returning 404, a wrong admin password returning 401 and an invalid payload naming the exact field are useful signals, not inconveniences.
6. Deploy one surface and verify production
Production deployment should preserve environment variables and durable data, create a backup, replace only the intended service and confirm that the public domain serves the new version.
The final check is external: open the real route, verify the important API and confirm neighbouring services are still responding. That final step is what turns a local implementation into a shipped product.