What Muse Spark 1.1 Taught Us About Enterprise Agent Architecture | INT21 Skip to content<br>Request access -> Research July 9, 2026 On this page<br>01 The test: Swarm research<br>02 What the workload exposed<br>03 What the three reports agreed on<br>04 What should we do differently<br>05 The larger signal<br>06 Download the full reports<br>On July 9, Meta released Muse Spark 1.1, a multimodal reasoning model built for agentic work, along with the public preview of the Meta Model API. We placed it inside Swarm research and gave it an ambiguous market question. Twenty-nine agents, 40.5 million tokens, and roughly 30 minutes later, it produced a usable report. The more important result was what the surrounding system had to learn before the model could do that reliably.

The test: Swarm research

SwarmOS is INT21’s cloud-native platform for coordinating large populations of agents. It already powers PTX Kernel Factory. Swarm research applies the same architecture to research: decompose a question recursively, search in parallel, preserve evidence, reconcile contradictions, and synthesize an auditable answer. We used our agent swarms to conduct research on various of high value topics.

We asked three model configurations the same question:

Research question

Huawei can build optical telecommunications systems. Is it a risk factor for optical networking inside data centers, and what could that mean for Lumentum and Marvell?

The run telemetry was:

GPT-5.6-Sol-max: 44 agents, 581,151,849 tokens, about five hours.

GPT-5.6-Terra-xhigh: 17 agents, 35,875,171 tokens, about one hour.

Meta Muse Spark 1.1-xhigh: 29 agents, 40,500,345 tokens, about 30 minutes.

This was not a controlled benchmark. Agent counts, prompts, token budgets, evidence cutoffs, and backend behavior differed, and token accounting can vary across APIs. The useful comparison is operational: cost, latency, evidence quality, and review effort per trusted result.

What the workload exposed

Muse Spark did not become productive through a model swap alone. The first runs exposed three integration gaps:

Recursive delegation had to be explicit: a subagent could launch more agents, yield while they worked, then resume and synthesize.

Workspace boundaries had to be explicit: agents operated in isolated environments, not on one shared computer.

API compatibility was not behavioral equivalence: Meta’s API was familiar enough for a fast integration, but this workload still required three generations of the backend adapter.

SwarmOS used those failures to improve delegation instructions, runtime assumptions, handoffs, and provider integration. One operator drove the adaptation over a few hours.

That is what self-improving means here. The foundation model’s weights did not change. The system around it learned how to assign work, recover from failure, and make the next run more reliable.

What the three reports agreed on

We reviewed each report against the other two rather than treating any one output as ground truth.

Sol was the strongest final synthesis. It separated documented Huawei 800G capability from unproven global hyperscaler qualification and public 1.6T leadership. It also treated Lumentum’s risk as indirect and Marvell’s exposure as two-sided because competing module vendors may still use Marvell silicon.

Terra provided the cleanest conservative baseline. Its July 9, 2025 evidence cutoff established Huawei’s capability but did not find proof of non-Huawei qualification, merchant scale, hyperscaler adoption, or direct displacement of a named Lumentum or Marvell program.

Muse Spark produced the broadest map of regulation, supply chains, technology roadmaps, and scenarios. It concluded that Huawei is a credible direct competitor in China and selected non-U.S. markets, while the wider Chinese optical ecosystem may create more immediate global pressure. Greater use of secondary sources and stronger inference increased the review burden.

Despite different methods and evidence cutoffs, all three reached the same bounded conclusion: Huawei is a real optical competitor, but the risk depends on product layer, geography, qualification, regulation, and supply chain. Capability is not the same as proven global commercial scale.

What should we do differently

Treat the control plane as the durable asset

Models will change faster than enterprise systems. A control plane that can observe failures, adapt orchestration, and integrate a provider in hours reduces model lock-in. The strategic choice is not one permanent model; it is a governed portfolio behind stable interfaces.

Route models by role

The fastest worker, the best evidence reviewer, and the most conservative synthesizer may be different models. Swarm architecture makes that a routing decision instead of an all-or-nothing vendor decision.

Measure cost per trusted result

Token price is only one input. Enterprise evaluation should include elapsed time, failed calls, source quality, contradiction handling, reviewer...