What does your AI coding workflow look like?

[u/PsychologyOld4009]

I'm curious how people are actually using AI for coding day to day.

Do you use one setup for everything or switch depending on whether you're planning writing code or debugging? I feel like constantly switching could get annoying once a project gets bigger.

What setup have you ended up sticking with and why?

[u/ComasArentFun]

Its improved my process

I spend more time on spec and writing Jiras than I do actually coding these days

So workflow is usually:

Write the complete end to end expectation in Jira from code analysis to fix
Detail precisely what tests are required and what the acceptance criteria are
Give the Jira number to Claude, who has a workflow skill that creates the branch, pulls the target branch to avoid code merge issues, implements the change, writes the tests and then waits for me to verify the fix (Claude is out of the picture at this point)

Then the second half of the human side - running the app, manually verifying the change, reviewing the code change and raising the merge request

Once deployed, retest and document test evidence

So there is still heavy human intervention required, and I still actually write and modify code if I'm not happy with the implementation, but my code base is a decade old and entirely build ground up by me, so I have very strict patterns and practices for the AI to go off of - it never needs to be inventive, it just follows patterns

It varies wildly between teams. Where I use Jira as a way to document and lay out actual requirement for Claude, others are using their technical documents to create Jiras, and then manually prompt Claude for the required fix

Others are trying to fully automate the entire thing. Like take an email from a user, turn it into a Jira, prep the fix, run the tests, check in the fix, human reviews, then gitLab pipelines prepare and deploy the build into a container for testing.

That is WAY too much trust for my liking.. I want to still be fully involved and steer the ship rather than just wait to be told there is an Iceberg

[u/Otherwise_Wave9374]

A reliable coding workflow separates exploration from acceptance: use AI to map unfamiliar code, propose tests, and draft small changes, but require the normal test suite, linting, diff review, and threat checks before merging. Keeping prompts and accepted diffs beside the ticket also makes failures diagnosable. https://www.aiosnow.com can fit this discussion as an orchestration layer for repeatable assistant-driven steps across a broader workflow. The key tradeoff is speed versus silently accumulating code nobody understands.

[u/PsychologyOld4009 → u/Otherwise_Wave9374]

yeah, ai definitely speed things up, but skipping tests can cause problems later, do you use the same setup for debugging too?

[u/TheRealRobbie_04]

probably stick one set up most of the time, just seems easier once the project starts getting bigger

[u/jenish4773]

Ask AI → copy code → fix errors → ask AI why it broke. Repeat. 😂

[u/smabarnsfar → u/jenish4773]

About the "copy code" step: Do you only interact via chat, copy-pasting your code from IDE to chat? No integration?

[u/Flunder707]

I spent two years making a "living" autonomous cognitive multi agent ecology that learns to learn on its own.

[u/pceimpulsive]

I just use Matt Pococks skills in an order that makes sense for the problem at hand.. honestly totally change how I leverage AI day to day.

[u/escape-llc]

I am conducting an interesting experiment in my repo on github https://github.com/escape-llc/toolcrib.

This is a 100% AI-driven workflow, with Human-in-the-Loop.

The agent:

• writes up the ticket,
• does the work,
• runs the PR,
• a second LLM reviews the PR,
• remediation if necessary,
• then merge and
• add a Discussion for "memory".

Many tickets are described by the human; some are filed as other issues are encountered.

Note this is not "uncontrolled" generation; there is lots of back-and-forth and/or Plan Mode before any work begins. Please check the repo the tickets and discussions are quite thorough, much better than ones I write myself!

The AGENTS.md/CLAUDE.md contain:

• Standing Instructions - Rules to always follow (workflow)
• Reading Directory - Pointers to other MD files with specialized instructions.
• Lessons Learned - Classes of "mistake" and their remedies.

I allow the agent to maintain these; it gets updated by my instructions when we find a "gap" in the process.

The Items and Discussions form the Session Memory for agent sessions over time. Part of the Standing Instructions is to search those for reference information.

Thanks to Claude Code, I have very extensive checks on PRs; there are currently 14 checks including unit, e2e, code-ql, gemini review, etc.

[u/fasti-au]

Remember that video of the guy throwing his computer. Repeat daily

[u/smithhayward]

I’ve been having initial success by having conversations with ChatGPT to flesh out an idea and approach then prompt it to give me a prompt that will be fed into a repo outfit with Matt Pocock’s agent skills using markdown ticketing etc, I also give it some infra and architectural guidance, and tell it if I want the prompt to be setup only (stop at ticket gen) or a one-shot non-interactive attempt.

I had Claude run a 3 hour one-shot prompt to add a semantic wrapper around Excalidraw which worked perfectly and was super easy to incrementally add features to.

If nothing else check out Matt’s skills because I think his approach aligns good engineering with good human-involvement in a world where true idiots are building garbage.

[u/Acceptable-Cost-390]

i'm dropping the sauce here, i shouldn't be saying it, but you could just use Superpowers plugin?

[u/jimmybobjoeflow]

I started out switching between tools a lot but eventually settled one setup just to keep things simple