Re: Discussion: Future-Proofing Git for Massive AI Parallelism

[Tanish Desai #TD <tanishdesai37@gmail.com>]

> On 20 Jul 2025, at 6:11 PM, Skybuck Flying <skybuck2000@hotmail.com> wrote:
> 
> Dear Git Community,
> 
> I’d like to spark a conversation about the evolving demands on version control systems in the age of AI -
> specifically, massive parallel processing and collaboration among swarms of autonomous AI agents.
> 
> Git’s architecture is rock solid for human developers, but when scaled to the synthetic masses, some limitations start to bite.

Yes this is true and I am also conservative about it. 
I was building a product which collaborated multiple AI Agents
and git failed miserably.

> 
> Challenges We’re Facing:
> 
> - Human-Centric Workflows:
>   Commits, branches, merges—great for humans. But when thousands of AI agents try to play ball,
>   Git feels like it’s hosting a developer convention inside a phone booth.
>  
> - Large Binary Assets:
>   AI projects sling around multi-gigabyte models and datasets like frisbees. Git LFS helps, but it’s struggling in the big leagues.
>  
> - Conflict Resolution at Scale:
>   With thousands of agents updating stuff 24/7, merge conflicts become a cosmic horror. Human-driven resolution? Not scalable.

Many AI agents now-a-days support automatically
resolving merge conflict using LLM but why
not adding a native support in Git itself?

>  
> - Authentication Overload:
>   Static credentials and manual account setups don't scale when every AI agent needs dynamic, role-based access.
> 
> - Semantic Blindness:
>   Git tracks text, not meaning. AI changes like hyperparameters or architecture tweaks need smarter, semantic versioning.
>  
> Potential Paths Forward:
> 
> Short-Term:
> 
> Supercharge Git via smart tooling:
> 
> - Tighten integration with MLOps systems like DVC, MLflow, LakeFS:
> 
>     These tools specialize in handling the chaotic realities of AI development—massive datasets, frequent experiments, and ever-evolving model versions.
>     By deeply integrating Git with them, we can:
> --- Offload Large File Management: Let DVC or LakeFS handle model binaries and datasets with scalable storage backends, while Git focuses on code.
> --- Track Experiments Natively: MLflow records hyperparameters, metrics, and artifacts—linking them directly to Git commits provides rich reproducibility.
> --- Enable Smarter Merges: AI-native tools can inform merge decisions based on model performance metrics or semantic changes, not just line-by-line diffs.

3-way diff merge is not an solution for AI generated 
workloads mainly because they are so fast at 
generating code.

3‑way diff merges assume that changes happen 
at human speed: you fetch the latest remote state, 
make your edits, and then merge and push before
anyone else has moved the branch. 

But AI agents operate orders of magnitude faster. 
By the time one agent fetches, modifies, merges, and pushes, 
another agent has already updated the same files—
so every “merge” either conflicts or silently overwrites prior work.

> --- Facilitate Parallel Agent Workflows: These platforms already support multi-run and multi-agent tracking. Git can lean on them to orchestrate agent commits
>     without bottlenecks.

A simple strategy many multi‑agent systems (and I) use is file‑level locking,
which Git doesn’t support natively:
on Linux, wrap your merge command with 
flock /path/to/file.lock -c "git merge origin/main”(only files involving the merge),
and require all agents/people to also use the same 
flock /path/to/file.lock when editing that file—
if it’s locked by the merge, they’ll automatically wait, 
then resume against the updated version.
But this strategy slows down AI agents. 

> --- Unify Dev & Ops Pipelines: A tighter link between version control and operational tools helps automate everything from data prep to deployment.
> --- If Git becomes more than just a file versioning tool and evolves into a smart orchestration layer, integrating these systems could turn it into the
>     central nervous system of AI development.
> 
> - Create orchestration layers for automated agent commits and batching:
> 
>     When thousands of AI agents are making changes simultaneously—whether to code, models, or config files—it’s chaos unless there’s a system coordinating
>     those contributions. Orchestration layers act like traffic controllers, guiding when, how, and what agents commit.


Even I share that vision: a Git redesigned for AI fleets.
The final goal would be to develop Git to a level 
fleets of AI agents could seamlessly use it to build
projects which earlier took decades to complete 
now could be build in weeks by agents.
Also thanks for mailing this : ) 
> 
> 
> 
>     What These Layers Would Do:
> --- Batch Commits: Instead of every agent making atomic commits constantly (leading to performance overload and conflict central), the system groups related
>     changes together and pushes them as unified commits.
> --- Schedule and Prioritize: Not all agents are equal. Some are more critical or trusted. An orchestration layer can schedule their commits based on priority,
>     timing, or dependencies.
> --- Conflict Mitigation: Before committing, the system checks for overlaps and intelligently merges or staggers updates to reduce merge hell.
> --- Audit and Rollback: It can log which agent did what, allowing transparency and reversibility if something breaks.
> --- Meta-Agent Oversight: You could even create supervisor AI agents whose job is to monitor and optimize commit behavior across the fleet.

fleet is the key!

> 
>     Why It's Important:
> --- Without orchestration, it's like 10,000 bots trying to edit a document at once. Git wasn't built for that kind of speed or concurrency.
> --- This layer turns AI collaboration into a harmonized symphony, instead of a noisy code stampede.
> 
> If Git had built-in support for this kind of orchestration—or if a wrapper system implemented it—you could revolutionize how synthetic intelligence collaborates at scale.
> Want to brainstorm what these meta-agents or orchestration rules would look like?
> I’m loaded with ideas.
> 
> - Improve tracking/versioning of AI-native assets: configs, metrics, logs
> 
> Long-Term: Consider an “AI-Native” versioning system
> - Semantic conflict resolution powered by AI
> - Native support for large models and datasets
> - Dynamic permissions for AI agents without static user accounts
> - Graph-based, event-driven change tracking beyond linear commit history
> 
> Let’s explore what’s possible. Whether it’s evolving Git or drafting a next-gen system, your expertise could help shape how AI collaborates at scale.
> 
> Thanks for reading—and yes, no rogue AI has committed rm -rf /… yet.
> 
> Sincerely,
>   Skybuck Flying