A good AI coding workflow is not a pile of clever prompts. Between the idea and the code there should be a chain of artifacts that can be checked, handed to another agent, and resumed without losing meaning.
What chain makes AI coding verifiable?
A useful loop looks like this:
- setup: prepare the environment, project rules, and skills;
- interview: remove ambiguity before work starts;
- spec: fix the goal, constraints, and acceptance criteria;
- tickets: split the path into small checkable tasks;
- implementation: let the agent work inside clear boundaries;
- TDD: check behavior with tests, not model confidence;
- fresh-context review: review the result in a clean context;
- manual QA: inspect what the user will actually see.
The main mechanism is avoiding one endlessly growing chat. Long sessions degrade over time, so important decisions should live in specs, tickets, context files, and checks.
The agent can research, write, and repair, but it should return with a compressed and verifiable result.
How does the same loop apply to editorial work?
An email or batch item should not become a public post in one jump. First capture sources and facts. Then formulate the argument. Then draft. Then clean style. Only after that should a preview be shown for explicit publication.
That lets the next agent continue from recorded state instead of guessing what happened in the previous chat.
What should operators remember?
An agentic workflow should materialize intermediate states. Otherwise the long chat becomes the system’s only memory.
This is overkill for tiny edits. It pays off when the work affects public posts, code, migrations, or production actions because mistakes are caught before publication or merge.