Over the past decade, I spent my career building consumer and cloud platform core systems operating at hundreds-of-millions-of-users scale—where reliability, latency budgets, and permission boundaries are non-negotiable engineering laws. Later, as an early-stage technology investor evaluating well over a hundred AI startups from first principles, I kept witnessing the exact same structural wall.
Foundation models got dramatically better at reasoning, yet enterprise productivity barely budged outside software engineering. Why? Because the first wave of AI assumed the enterprise world is cleanly exposed via REST APIs. In reality, roughly ninety percent of high-stakes operational execution happens where no public API exists—inside mobile-native apps, encrypted messaging threads, legacy desktop clients, and multi-party human workflows.
The second half of AI does not belong to another chat sidebar. It belongs to governed terminal execution—measured in closed loops, protected credentials, and auditable unit economics.
Organizations tried bridging this gap with two flawed compromises: chat wrappers that stop at drafting text and leave humans as copy-paste middleware, or brittle headless scraping scripts that exfiltrate session cookies to the cloud and trigger immediate risk bans. Neither architecture is acceptable for an institution with real fiduciary responsibility.
We built Tarmac to be the missing sovereign execution layer. By combining a declarative Agent Operating Procedure (AOP) compiler, physiologically faithful behavioral pacing on real desktop and ARM64 mobile hardware, a zero-cloud 127.0.0.1 permission broker, and inline human approval gates, Tarmac turns frontier intelligence into finished, accountable work.