Yes—iterating using test failures is one of the most practical and reliable ways to use GPT 5.3 Codex. The workflow is simple: apply a patch, run tests, feed the failing output back, and ask for the next minimal patch. This is exactly how human engineers debug, and it’s why “agentic coding” matters: the model isn’t expected to be perfect on the first try; it’s expected to converge under feedback. OpenAI publishes a dedicated cookbook showing how to embed the Codex CLI into GitHub Actions so that when builds/tests fail, Codex proposes fixes—this is a concrete, official example of test-driven iteration in automation. See: Autofix GitHub Actions with Codex CLI. OpenAI’s automation guidance also explicitly recommends looking for failures and running the smallest relevant verification, reinforcing the same loop: Codex automations guidance.
To make test-failure iteration productive (and not a time sink), enforce a strict “one change at a time” discipline:
Provide only the necessary failure output (the failing test name, error message, and stack trace).
Ask for a hypothesis + minimal fix (what line, why it fails, what change).
Require a diff (not just code snippets).
Run only the smallest relevant test first (single test file or specific test target).
Escalate gradually (full suite only after targeted tests pass).
If tests are slow, instruct the model to prioritize “fast checks first” and to avoid changes that increase scope. You can also ask it to add or adjust tests when the failure indicates missing coverage. The agent loop works best when the model has a clear stopping condition: “Stop when npm test passes and no lint errors remain.”
In RAG-heavy developer tools, test failures are often paired with doc retrieval: a failure might be caused by using an API incorrectly or missing a version-specific behavior. Store your API docs and troubleshooting guides in Milvus or Zilliz Cloud, retrieve the relevant sections based on the error message, and include them in the prompt alongside the test output. Then GPT 5.3 Codex can fix the code while staying aligned with documented behavior. This reduces “fixes” that merely silence the test without addressing the real issue, and it makes the iteration loop converge faster because the agent has both evidence (test failure) and authority (retrieved docs).