Stop Waiting for a Bigger Context Window | INT21 Skip to content<br>Request access -> Engineering July 7, 2026 On this page<br>01 A Bigger Window Is Not Better Context<br>02 A General Solution for Complex Problems<br>03 Long Context Is Becoming a Systems Problem<br>At INT21, we are all-in on self-improving multi-agent systems. We have built SwarmOS, our cloud-native platform for running specialized agents, and our first product, PTX Kernel Factory. The biggest change is not simply a larger context window. It is what frontier models make possible when they are orchestrated: turning one enormous context problem into a coordinated team of smaller, evidence-seeking tasks.

About a year ago, I was exploring how AI agents could generate CUTLASS C++ kernels, NVIDIA’s building blocks for high-performance GPU computation. By my count, the entire CUTLASS codebase represented roughly five million tokens. At the time, the best production model available to us offered a one-million-token context window.

The central blocker was never code generation. It was finding and preserving the right evidence across the repository.

Rather than wait for a magical five- or ten-million-token model, I ran the one-million-token model several times in parallel. Each agent studied a different portion of the codebase, and combined their findings as the final step.

It was a simple architecture, but it established the principle behind our work today:

When context stops fitting vertically, scale it horizontally.

A Bigger Window Is Not Better Context

Even when millions of tokens technically fit inside a model, the model must still separate signal from noise. A bigger window introduces more irrelevant information, more intermediate output, and more competition for attention.

Multi-agent systems address this structurally. Specialized agents explore different parts of a codebase, investigate the same question from independent angles, and return distilled findings to a coordinating agent. When a subproblem is still too large, it gets divided again.

The goal is not infinite context. It is effective context .

A General Solution for Complex Problems

At INT21, we use SwarmOS not only for hard engineering problems, such as expert-level PTX generation, but also to understand complex business landscapes.

To test this outside a codebase, we pointed SwarmOS at a different kind of long-context problem:

Research question

Could Huawei, CXMT, and China's AI stack create pressure on Western AI compute economics?

The research ran autonomously using public information. The system involved 27 agents, performed 166 web searches, visited more than 200 web pages across 73 unique domains, and ran for about two hours.

In total, it consumed 119 million tokens.

We are sharing the report in this article because we believe this is a topic many people will want to understand more deeply. But the report is also a demonstration of the broader point: multi-agent orchestration is the real long-context breakthrough.

Long Context Is Becoming a Systems Problem

So, are the latest AI generations solving long contexts?

Not by making context infinite, but in an agentic way.

It is helping solve long context by making it divisible, searchable, and composable .

That is why INT21 is all-in on multi-agent systems. At INT21, we are building Self-Improving Compute Infrastructure, and SwarmOS is the operating system behind a massive number of agents.

PTX Kernel Factory is now in beta for teams working on GPU kernel generation and AI compute infrastructure. Accepted participants receive limited-time free access and $100 in credits. Join the beta ->.

Huawei, CXMT and the China AI-stack pressure path

Evidence snapshot: 2026-07-06 UTC<br>Research support only. Not investment, legal, procurement, operational, or tax advice.

Download full report<br>+ evidence packet<br>(3.0MB, 103 files)

The research supports a plausible regional pressure scenario, not the sensational claim that China has already broken NVIDIA, HBM and neocloud scarcity economics worldwide.

Final answer

Yes, this could become a dislocation path. The supported version is regional price-anchor pressure with financial transmission risk.

Swarm stats

27 agents, 119M tokens

166 searches, 200+ public pages, 73 unique domains, and about two hours of elapsed runtime.

27agents in the system

166web searches conducted

200+web pages visited

73unique domains

~2happroximate runtime

119Mtokens consumed

Bottom line<br>Pressure chain<br>Evidence quality<br>Main findings<br>Thresholds<br>Ascend TCO<br>Watch indicators

Mechanism map

A pressure chain, not a completed displacement

The strongest mechanism is not a sudden global cancellation of Western GPU-cloud contracts. It is a chain that starts with China-local substitution and low token prices, then travels into renewal terms, new-build economics, financing spreads and private marks.

Huawei/Ascend surfaces appear real enough for selected China-local or Huawei-controlled workloads. CXMT is a real...