Mistral launched Workflows. OpenAI has spent months building orchestration layers. Anthropic is no longer just a model—it's the environment where that model operates. In twelve months, the leading AI labs have quietly pivoted: from building models to building runtime.
This isn't a minor technical footnote. It's a business shift that should change how your company thinks about these platforms.
- Labs are no longer competing only on model quality—they're competing to become the operating system for your automated workflows.
- The more you integrate their orchestration layers, the harder it is to leave. Lock-in has moved from data to process.
Dependency: The New Lock-In Isn't the Model
For years, the AI lock-in argument revolved around data: if your interaction history lives on a proprietary platform, switching providers is painful. That risk hasn't gone away—but a new one sits on top of it.
When you adopt a lab's agent system, their workflows, persistent memory, and tooling, you're building business logic on top of their execution infrastructure. Not on an interchangeable model, but on an environment that defines how tasks are chained, how context is managed, and how each call is priced. Migrating that isn't swapping an API—it's rewriting entire processes.
This is why local-first architecture deserves a second look—not as technical purism, but as an operational sovereignty strategy against platforms that increasingly resemble proprietary operating systems.
Strategy: What to Do When the Lab Wants to Be Your Infrastructure
The answer isn't to reject these platforms. It's to enter with clear eyes and a deliberate abstraction layer.
What we recommend at Room 714 is drawing a hard line between two layers: orchestration logic (which tasks chain together, under what conditions, toward what goals) and model execution (which LLM or agent handles each specific task). The former must live in your own versioned, portable code. The latter can live wherever makes sense at any given moment.
If your business logic is coupled to the lab's runtime, you don't have an AI strategy. You have a dependency with good PR.
This connects directly to the case for specialised over general-purpose models: just as smaller, focused models reduce cost and restore control, designing portable orchestration reduces exposure to a vendor who is today's best option but may change pricing or terms tomorrow.
If you're assessing how to integrate agents or automated workflows into your product without ceding architectural control, that's exactly the kind of conversation we have at Room 714. Before you build on someone else's runtime, it's worth knowing precisely what you're giving up.
