By this point, the problem should be clear.
Deep AI workflows are expensive not because models are slow, but because the same work is repeated across steps. Verification gets disabled. Costs scale with depth. Teams eventually self-host just to regain control.
CLC exists to address that problem—at the execution layer.
This post is part of a series on the economics of multi-step AI workflows. We examine why inference costs scale with depth, why verification is disabled in production, and why existing optimizations fail to eliminate redundant execution across workflow steps.
What CLC Is
CLC is execution-layer infrastructure for **multi-step AI workflows**.
It allows systems to carry execution forward across steps instead of restarting inference from scratch each time.
That single change alters the economics of agentic systems:
- Shared context doesn't need to be reprocessed repeatedly
- Verification becomes affordable
- Workflow depth stops multiplying cost
CLC operates below agent logic and above model runtimes. Agent behavior stays the same. Models stay the same.
Only execution changes.
What CLC Is Not
To be explicit, CLC is not:
- A new model
- A prompt-engineering technique
- An agent framework
- A replacement for inference optimizations
- A hosted API abstraction
CLC doesn't decide what an agent does. It doesn't change how models reason. It doesn't require rewriting workflows.
It changes how work is executed across steps.
Who CLC Is For
CLC is built for teams that already feel the pain:
- Running multi-step or multi-agent workflows
- Working with large shared context
- Seeing cost scale with depth, not traffic
- Disabling verification to stay within budget
- Evaluating or already operating self-hosted inference
If your system is shallow or single-turn, CLC won't matter yet.
What Changes When CLC Is Present
With execution handled differently:
- Context is processed once, not repeatedly
- Verification loops become viable by default
- Costs scale with value, not step count
- Optimization compounds instead of repeating
The system behaves the same. The economics don't.
Understanding why LLM inference cost scales with depth helps explain what CLC addresses. The problem becomes clear when you see how AI verification loops get disabled due to cost structure.
CLC Labs is working with a small number of teams to validate this approach on real workflows.