For people using AI coding agents in real codebases, I’m trying to understand the actual workflow — not the hype version. When you give an agent a task, what usually happens?
- Do you write a detailed plan/spec first?
- Do you give it a short GitHub issue and let it figure things out?
- Do you review mainly after the PR/diff is done?
- Do you break work into tiny tasks because larger ones get risky? I’m especially curious where your time goes:
- How much time do you spend planning before the agent writes code?
- How much time do you spend reviewing/fixing after it writes code?
- At what point do you stop trusting the agent?
- What mistakes happen most often?
- scope drift
- wrong assumptions
- touching unrelated files
- missing tests
- passing CI but still doing the wrong thing
- messy PRs
- hard-to-review diffs What are you currently doing to make AI-written code safer?
- strict prompts
- checklists
- CI/tests
- manual PR review
- asking the agent for a plan first
- limiting file access/scope
- smaller issues
- another agent reviewing the first one
- something else? One thing I’m trying to figure out: If you wanted 99% confidence before merging AI-written code, what would need to be true? For example, would you want:
- a better pre-coding plan?
- a way to lock the agent to approved scope?
- proof of what tests/checks it ran?
- a summary comparing the final diff against the original issue?
- a warning when the agent touches unrelated files?
- a trust score/check on the PR?
- something more like CI, but for agent behavior instead of just tests? Also: would adding this kind of gate feel useful, or would it feel like annoying process overhead? Trying to learn how people actually work with coding agents today, and what would make them trustworthy enough for serious team usage. submitted by /u/Few-Ad-1358
Originally posted by u/Few-Ad-1358 on r/ClaudeCode
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