autonomous-development-workflow

Autonomous Development Workflow

For AI agents completing tasks without human supervision. The goal: deliver a clean pull request that passes all checks and gets merged without back-and-forth.

Spec Quality Is the Bottleneck

Invest more time understanding the problem than writing the implementation. A precise understanding of the problem produces better code than any amount of iteration on an ambiguous one. If the task description is ambiguous, clarify before implementing. Quick, localized changes with no behavioral impact may proceed directly.

Before Implementation

Assess whether you have enough clarity to implement correctly. Can you articulate the problem being solved, what "done" looks like, the edge cases, and the assumptions you're making? If not, ask before proceeding.

Load project standards via /load-rules. If that's not available, fall back to reading applicable rules directly. Every applicable rule must be followed.

If CLAUDE.md or AGENTS.md exist in the project root, read those for additional context.

Implementation

Write code following all cursor rules. Reference specific rules by reading the files directly.

Validation - Use the Tooling

The project has tooling configured. We use it instead of manually trying to comply.

Check for pre-commit and run if exists:

[ -f .pre-commit-config.yaml ] && pre-commit run --all-files

Read .github/workflows/build.yml (or ci.yml, test.yml) and replicate those validation steps locally, for example:

ruff check --fix .    # Auto-fix linting
ruff format .         # Auto-format code
pytest                # Run tests

If we added functionality, we add tests following project patterns. Aim for 95% coverage -- solid testing without obsessing over every edge case.

Only commit and push when all validation passes.

Evaluate Outcomes

Run git diff and review the changes at the feature level. The question isn't "is every line correct" -- it's "does this changeset solve the problem cleanly?"