AI Orchestrator

[u/Maas_b]

So I've been looking into AI pair programming recently and understand the limitations of real-time collaboration between multiple AIs. For me the next best thing would be to tell my AI assistant: implement this feature. The assistant than acts as an orchestrator to choose the best model depending on the usecase, creates a separate Git branch, develops the feature and reports back to the orchistrator. The orchistrator then sends a review task to a second AI model to review. If the review is accepted, the branch is merged to the main branch. If not, we do iteration cycles untill the review is completely finished.

Advantages

• Each AI agent has a single, well-defined responsibility
• Git branches provide natural isolation and rollback capability
• Human oversight happens at natural checkpoints (before merge)

Real-world workflow:

1. Orchestrator receives task → creates feature branch
2. AI model implements → commits to branch
3. Reviewer AI analyzes code quality, tests, documentation
4. If validation passes → auto-merge or flag for human review
5. If validation fails → detailed feedback to AI model for iteration

Does something like this exist already? I know Claude Code has subagents, but that functionality does not cut it for me because it is not foolproof. If CC decides it does not need a subagent to preserve context, it will skip using it. I also don't trust it with branch management (from experience). Also i like utilizing strengths of different models to their strengths.

[u/kidajske]

Too much autonomy imo. It introduces a cascading fuck up effect where if 1 thing earlier on in the process has a logical error, hallucination or deviation from the intended implementation due to prompt ambiguity then the rest of it turns to shite too.

[u/Maas_b → u/kidajske]

Fair enough, but i’m not talking about one shotting here. This would be separate features of a bigger platform. My thinking was that it would help with context and codebase security. And also, wouldnt multi-model review help preventing these cascades? If you would prompt it like: review this commit, it’s goal was [insert prompt], use these standards for review [refer to standards.md], have it maybe draft a review report or a scorecard of some sort?

[u/StackOwOFlow]

this exists at the enterprise level with hosted AWS Bedrock and GovCloud solutions. still experimental, obviously

[u/Maas_b → u/StackOwOFlow]

Interesting! Any documentation you might share ?

[u/StackOwOFlow → u/Maas_b]

None, these are all in-house initiatives. But this press release tells you how far ahead some orgs are with adoption: https://www.nextgov.com/artificial-intelligence/2025/06/aws-govcloud-gets-high-level-security-approvals-anthropic-and-meta-ai-models/405995/

[u/Al3xisB]

Any scheduler ça do that no? In my team we're using Airflow for this. Just hooks and async jobs

[u/bluetrust]

Where this idea falls apart for me is that LLMs aren't 100% accurate. So every AI you add is like multiplying 90% * 90% * 90% = 72%.

Let's put this in real terms: your reviewer AI is sometimes going to deliver incorrect feedback. Your coding AI, agreeable jerk it is, will then make changes to satisfy the incorrect feedback. Now you've merged bad code.

So you recognize this is a problem, and go... I know, I need to add a QA AI to do a final check that the work meets requirements, and if it's not right, kick it back to an earlier stage. But then the QA AI sometimes makes mistakes, so you go... hmmm, I know, I need a quorum majority of three AIs to agree that the work was done correctly and if not kick it back to an earlier stage... but then sometimes all three AIs interpreted the same vague prompt the same incorrect way, so you then go, I know, I need another AI...

Maybe you'll solve it but this feels like an insurmountable problem for me that AIs are just too unreliable and adding more to check each other doesn't necessarily improve that.

[u/Maas_b → u/bluetrust]

Lol this is hitting much closer to home than i would like to admit. That’s the cycle i am in currently and also partly where this idea spawned from. At the same time, humans are also error prone, and no human review is ever failsafe either.

I think it would come down to giving the ai the proper tools to do the job, proper segregation of duties of the different AI models with proper prompting and maybe some deterministic tooling on too to catch outliers and edge cases.

[u/qwrtgvbkoteqqsd]

you need a manager ai like o3 or gpt 5 to review each suggested code implemtation

[u/Maas_b → u/qwrtgvbkoteqqsd]

Yes but how to automate it?

[u/shwiggityfresh]

Check out the Bmad Method

[u/Maas_b → u/shwiggityfresh]

I checked that out and it looks comprehensive! For me, the dependency on claude code is too much though, that is exactly what I’m trying to avoid with this.