How to Build an AI Coding Agent Workflow

1. Define the task and acceptance criteria

Start with observable behavior. For example, a CSV export should keep commas, quotes, and line breaks inside a single field. An empty result should still include the header. Those conditions are more useful than a broad request to improve the export feature.

Write down what the agent may change, what needs a human decision, and how you will check the result. Ask it to inspect the repository instructions and existing implementation before editing. Keep unrelated changes outside the task.

Input: a small example that demonstrates the problem.
Expected output: the behavior a user should see after the change.
Boundary: the files or subsystem involved and compatibility requirements.
Evidence: a test, command output, or manual scenario that can demonstrate success.

2. Plan and implement a bounded change

Have the agent explain the proposed change and its risks before implementation. In the CSV example, it should first find the existing serializer and check whether a shared library already handles escaping. The plan should name the behavior to preserve, such as column order and the download filename.

Use one writer for the same files at a time. Parallel work is easier to inspect when each participant has a separate area and a clear handoff. If a design question has several credible answers, a separate agent can challenge the proposal before code is written; a debate is optional and does not replace testing.

3. Run checks that prove the behavior

Choose checks from the repository and the changed behavior. For the export example, test a comma, an embedded quote, a line break, and an empty dataset. Parse the result with an independent CSV reader where practical so the check verifies usable output.

Record the actual command and result. A test that was suggested but never run is pending. A Windows check does not establish that a macOS build works. Keep remote CI, publication, and other external actions within the permissions agreed for the project.

After a failure, read the output and identify the cause before retrying. After a fix, check the changed behavior again and run any related checks needed to catch regressions. If the code changes after verification, determine which results need to be refreshed.

4. Review independently, then fix findings

Give a fresh reviewer the request, acceptance criteria, diff, and relevant checks. Ask it to inspect the code directly and report concrete defects with evidence. A summary written by the implementing agent is useful context, but it is not an independent review.

In the CSV example, a reviewer could check whether the export still works for large datasets, whether escaping is applied consistently, and whether spreadsheet formula handling needs a product decision. Each finding should describe a trigger and its consequence.

Fix blocking findings, rerun the affected checks, and return the changed area for review. Keep unresolved questions visible. Separate review adds another opportunity to catch defects; it cannot guarantee that software is correct or secure.

5. Close the task with evidence

Keep a short record of what changed, which checks passed, what was reviewed, and what remains unverified. Tie the record to the version of the code that was checked. Publication or deployment is a separate step when the project requires approval.

A useful closing record for the export task would name the escaping fix, the passing cases, the independent review result, and any remaining spreadsheet compatibility limits. That gives the next person enough context to maintain the change.

Choosing a desktop or CLI workflow

Garda AI Desktop provides a visual workspace for planning, agent debates, configured checks, independent reviews, and fixes. The public beta is available for Windows; its product page lists current requirements and licensing.

Garda Agent Orchestrator is the independent, open-source CLI option for governed coding-agent workflows. Choose it if you prefer working from a repository and terminal. The two products share a workflow philosophy and are separate tools.