Running multiple, complex AI coding agents simultaneously is computationally prohibitive on local machines. Stripe's success relies on their ability to spin up numerous isolated cloud development environments in parallel, a crucial investment for any team serious about agentic engineering.

Deploying AI agents in isolated business functions is a missed opportunity. True enterprise value is unlocked when agents share context (e.g., between sales and maintenance), enabling optimization across the entire organization, not just within a silo.

Developing with AI agents on your local machine creates bottlenecks and coordination nightmares. Cloud-based virtual machines (VMs) allow you to run numerous agents in parallel without code collision, drastically increasing your shipping velocity and making local development a relic of the past.

Inspired by fully automated manufacturing, this approach mandates that no human ever writes or reviews code. AI agents handle the entire development lifecycle from spec to deployment, driven by the declining cost of tokens and increasingly capable models.

To control costs, security, and governance, enterprises are moving from interactive, ad-hoc agent use to a 'software factory' model. This approach systematizes the entire work lifecycle, automating processes and minimizing the risks associated with inconsistent human operation of powerful AI tools.

To avoid chaotic spending, enterprises must replicate their "Cloud 2.0" governance models for AI. This means establishing a central platform engineering team to broker access to models, set budgets, and control the tools agents can use. This prevents runaway costs and security risks from decentralized AI development.

The current model of a developer using an AI assistant is like a craftsman with a power tool. The next evolution is "factory farming" code, where orchestrated multi-agent systems manage the entire development lifecycle—planning, implementation, review, and testing—moving it from a craft to an industrial process.

While local coding agents have product-market fit today, OpenAI's Michael Bolin argues the long-term trend is remote agents. To achieve true automation—like having an agent autonomously tackle every new bug ticket—workloads must run in the cloud, unconstrained by a developer's personal machine.

Running multiple AI agents in parallel quickly leads to "AI sprawl"—losing track of what each agent is doing, what they've accomplished, and how much they're costing. Orchestration tools solve this by centralizing tasks, tracking spend, and providing a unified management dashboard.