AI Coding Project Delivery: From Request to Verifiable Results

AI coding tools can generate code quickly, but generated code is not the same as a completed project. Common failures come from vague requirements, uncontrolled scope, missing tests, and handoffs that do not explain what changed or what remains risky.

This workflow is for independent developers and small teams using coding agents such as Codex or Claude Code. Scale the process to the risk of the task instead of applying every step mechanically.

A repeatable delivery workflow

1. Turn the request into acceptance criteria

Before editing code, clarify the user problem, required behavior, existing behavior that must not change, and observable conditions for completion. List unresolved assumptions instead of silently guessing.

“Improve the page” is not an acceptance criterion. “The primary action remains visible on mobile, keyboard navigation works, and the production build passes” is much easier to verify.

2. Inspect before planning

Ask the agent to inspect relevant files, configuration, tests, and existing conventions. The plan should identify files to change, likely side effects, validation commands, and risks.

For payments, authentication, migrations, production writes, or permission boundaries, define safety constraints before execution.

3. Implement in small, testable steps

Keep each change focused. Complete the critical path first, then edge cases and polish. If new evidence contradicts an assumption or expands scope, pause and clarify rather than silently doing more work.

4. Verify with tools, not self-assessment

Separate checks into four groups:

• Static checks: types, formatting, dependencies, and build.
• Functional checks: the core user journey.
• Regression checks: existing behavior that must remain intact.
• Handoff checks: documentation, configuration, migration steps, and known limitations.

A model saying “tests passed” is not evidence unless the commands actually ran. Mark unrun tests as unrun.

5. Make the handoff useful

Report what changed, key files, checks that ran, checks that did not run, known limitations, and how to roll back or continue debugging.

Reusable task brief

Goal:
User and scenario:
Acceptance criteria:
Existing behavior that must remain:
Allowed scope:
Questions to resolve first:
Implementation plan:
Validation commands and expected results:
Security and compatibility risks:
Required handoff format:

How to tell whether delivery is complete

Do not measure completion by lines of code or commit count. Check that each acceptance criterion maps to evidence, critical validations really ran, failures are reported honestly, changes can be rolled back, and another developer can maintain the result.

To make this process repeatable, start with the AI project delivery skill and pair it with the website launch QA skill before publishing.

Frequently asked questions

Does every task need the full process?

No. Small changes can use a shortened plan. Data migrations, permissions, production services, and broad refactors deserve stronger verification.

Why review tests generated by AI?

Tests can miss important requirements or simply encode the implementation's assumptions. Compare them with acceptance criteria and actual execution results.

How can I reduce token usage?

Inspect only relevant files, agree on a plan, run deterministic checks with scripts, and use the model for decisions that require reasoning. Re-sending the entire history for every step is rarely the cheapest approach.