Insights on Inference & Execution Efficiency

Most teams think they understand inference costs—until they build real agentic systems. Once workflows move beyond a single call, costs don't just rise. They compound.

Every serious AI team builds verification. They add checks, retries, evaluators. And then—once the system hits production—they turn most of it off. This isn't a failure of engineering discipline. It's a failure of the underlying cost structure.

AI infrastructure is full of optimizations. Faster runtimes, smarter batching, lower-precision weights, prompt caching. All of these matter. None of them solve the same problem.

Infrastructure teams measure inference costs obsessively. But there's a major cost driver that rarely shows up in dashboards until it's too late: redundant context reprocessing.

Most AI teams start with APIs. They're fast to adopt, easy to scale, and remove infrastructure from the critical path. But as systems mature and workflows deepen, something changes.

Deep AI workflows are expensive not because models are slow, but because the same work is repeated across steps. CLC exists to address that problem—at the execution layer.

Most AI workflows don't fail because models are weak. They fail because execution is wasteful. The simplest way to see this is to compare two mental models.

CLC is early. We're not launching a platform or an SDK. We're validating a structural change in how AI workflows execute.