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▲ 0 r/AI_Agents

People running coding agents across real repos: what breaks after the agent writes the code?

I’m seeing a pattern with teams adopting Claude Code, Cursor, Codex-style workflows, etc.

The coding step is not always the hardest part anymore. The harder part seems to be the layer around it:

  • Which tickets/tasks are safe for an agent?
  • How does the agent get the right repo context?
  • Who reviews the output?
  • How do you prevent secrets, migrations, infra changes, or risky refactors from slipping through?
  • How do you coordinate multiple agents without losing track of state?
  • How do you know whether your engineering org is actually ready for this?

I’m working on a readiness model for engineering teams adopting coding agents and would love feedback from people actually using them.

What would you include in an “AI engineering readiness” checklist?

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u/Huge-Advertising-951 — 8 days ago
▲ 5 r/Humber+3 crossposts

I made a Chrome extension for practicing ChatGPT-generated NCLEX-style questions without seeing the answer key first

Link here: https://chromewebstore.google.com/detail/chatgpt-quiz-mode/jaggacplpiopgdbhpbnfcpohnfjmplfj?authuser=0&hl=en

I’ve been working on a small Chrome extension for students who use ChatGPT to make practice questions.

One issue I noticed is that ChatGPT often gives the answer key and rationale immediately after the question, which makes it easy to accidentally spoil the answer before actually thinking through it.

The extension hides the answer/rationale, adds answer buttons, supports SATA-style questions, and lets you check your answer afterward.

I’m wondering whether this would actually be useful for NCLEX-style studying, especially for active recall and SATA practice.

I built it myself, so I’m mainly looking for feedback:

Would this help, or would you want it to work differently?

u/Huge-Advertising-951 — 9 days ago
▲ 6 r/platformengineering

Are AI coding agents creating a new platform problem inside engineering orgs?

I’m trying to understand how larger engineering teams are handling the operational side of AI coding tools.

A lot of teams seem to be adopting Copilot, Cursor, Claude Code, internal agents, etc., but I’m curious what happens after the first wave of adoption:

- Who decides which tools are allowed?

- How do you control repo/app access?

- How do you manage shared context, prompts, rules, and coding standards?

- Are teams tracking output quality, security issues, cost, or model usage?

- Does security/compliance care yet?

- Is this owned by platform engineering, DevEx, security, or individual teams?

I’m exploring whether there’s a real need for an “AI engineering control plane” for engineering orgs, or whether this is still too early / already solved internally.

For people at teams of 20+ engineers using AI coding tools: what’s actually painful here?

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u/Huge-Advertising-951 — 14 days ago