r/Agentic_Coding

I’m the maker of Superloopy, an MIT-licensed workflow layer for coding agents.

The pattern I’m trying to make practical is an “evidence gate” before an agent claims a task is done:

1. turn the task into explicit acceptance criteria

2. ask the agent to leave receipts under `.superloopy/evidence/`

3. use command-backed checks where possible, not just prose summaries

4. keep manual/visual proof separate from deterministic proof

5. finish with a report that says what passed, what still needs judgment, and where the artifacts are

It now works with both Codex and Claude Code. The implementation is intentionally thin: plugin hooks, skills/subagents, and a small CLI around evidence + final gates. The goal is not to create another agent, but to make existing coding agents easier to audit when they say “done.”

Repo:

https://github.com/beefiker/superloopy

I’m curious how other people building with coding agents structure this. Do you keep evidence artifacts? Do you require tests/screenshots/logs before accepting work? Or does that add too much ceremony for your workflow?

I built a small free macOS tool for reviewing Codex sessions using the Codex desktop app. Are people here using anything similar to improve their AI coding workflows?

After longer Codex runs, I kept finding that the transcript was technically available, but hard to review.

The things I wanted to inspect were:

- What changed

- Which files were touched

- Where tokens went

- Which tool calls mattered

- Whether the prompt/context was good enough to reuse

- What context would be useful to share during code review

So I made BuildrAI, a local-first app that turns Codex session artifacts into timelines, token usage, prompt/session evaluation, changed-file context, and shareable reports.

I’m curious how other people are handling this.

Do you review Codex sessions after the fact, or do you mostly trust the final diff?