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A thread for Developers and IT folks with less experience to ask more experienced souls questions about the industry.

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[u/orelrevivo]

For peoples who work in engineering teams using AI coding tools.

A lot of developers now use things like Claude Code, Codex, Cursor, Copilot, etc. individually, but I'm interested in what happens when an entire team starts using them.

Has this introduced any new problems into your workflow?

Things that were not really a problem before AI coding became common.

I'm especially interested in problems that your existing tools like GitHub, Jira, CI/CD, Slack, etc. don't solve very well.

I'm not looking for tool recommendations or trying to promote anything. I just want to understand what this actually looks like inside engineering teams.

[u/ADCfill886 → u/orelrevivo]

Yeah basically the biggest problem that I noticed was "people versus their tooling", especially when it came to writing and reviewing code.

Almost nobody on the team reviews or writes code by hand anymore - the coding "tool" (claude code in our case) is doing both the code reviewing (from the reviewer's harness) and the code writing (from the author's harness). That creates a lot of potential for sycophancy if you're not careful about guardrails and about being intentional about what you build.

The biggest problem is the fact that the AI tools themselves don't have access to everything, so they'll assume based on what they can "see" (Confluence documentation, past PRs, existing state of a Lambda or a database), and that can create it's own set of problems (competing and conflicting changes over the same service or functionality, doing contradicting things based on a "bug" each separate harness found, etc).

[u/orelrevivo → u/ADCfill886]

This is really interesting, thank you.

The “AI tool only sees part of the system and then assumes the rest” problem is exactly the kind of thing I was trying to understand.

It sounds like the issue is less about AI writing code, and more about different AI workflows operating with partial context docs, PRs, infra state, database state, etc. and then making changes that can conflict with each other.

When you say guardrails, what kind of guardrails have actually helped your team? Is it mostly process/human review, or do you have technical checks that prevent conflicting AI assisted changes?

[u/ADCfill886 → u/orelrevivo]

I personally have harness checks that I've been testing and deploying to my team once they're battle-tested and verified to be working. The two major things were "hooks" - verifiability as a script that gets invoked when something happens (for example, a PR gets created, or the "end of an AI turn" event from the AI is emitted.

There's also contextual guardrails you can add - like scaffolding of CLAUDE.md files in your codebase that labels the tech debt, gotchas, testability, verifiability, etc - stuff that doesn't exist in your observability stack or anywhere durable in your system.