Everyone’s chasing faster inference, lower latency, and bigger models.
But in production, speed stops mattering the moment your system crashes mid-workflow.
That’s the paradox I ran into again and again while building agentic AI systems.
The bottleneck wasn’t compute, it was fragility.
A single lost context window could derail an entire chain.
One async call could trigger a deadlock that looked “fine” in staging.
And every retry fix just made things harder to debug.
So instead of chasing more throughput, I started asking:
What if AI frameworks were built for stability first?
That question became a design principle for GraphBit, our open-source framework for agentic AI.
We built it from the ground up in Rust (for deterministic execution and lock-free concurrency)then wrapped it in Python (for developer accessibility).
It’s the balance that makes it powerful:
- Multi-LLM orchestration that doesn’t race itself
- State versioning for reliable context management
- Circuit breakers + retry guards baked into the runtime
The goal isn’t to make agents faster, it’s to make them reliable enough that you stop worrying about them.
Because “fast” means nothing when your system can’t finish the job.
If you’ve ever shipped an AI workflow that ran perfectly in a demo but collapsed under load, I’ve been there too.
Let’s talk about how to fix that for good.
🔗 github.com/InfinitiBit/graphbit
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