Originally published at: Building for the Rising Complexity of Agentic Systems with Extreme Co-Design | NVIDIA Technical Blog
Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don’t follow a pre-determined sequence of actions. They call tools, spawn sub-agents with different tasks and models, retain information in memory, manage their own context window, and decide for themselves when they’re finished. In doing…
This “seven chips and a software stack” co-design presents a compelling approach to addressing latency bottlenecks in agentic coding workflows.
Looking at where the co-design boundary sits, treating the software representation as a fixed constraint forces the silicon stack and cache harness to bear the entire burden of managing decode latency, KV-cache footprint, and sequence overhead.
Might an “extreme co-design” need to extend beyond the software stack here to the code representation itself?
If agentic systems shift from unstructured text streams to normalized, structurally verified graph substrates, the representation achieves far higher information density per token by enforcing grammar structurally. Done brilliantly, that should relieve KV-cache pressure, boost effective throughput, and align software-level structural predictability with LPX’s hardware-level timing determinism.
We explored this exact boundary — treating the substrate representation as a primary co-design variable — in our recent preprint, The Biomimetic Architecture of Software 4.0 (arXiv:2606.04025).
I’d love to hear how you view the interplay between hardware-layer determinism and the structural representation of the code graph. Does your team see this even more extreme co-design coming sooner or later?