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.
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.
The Default Execution Model
In most systems today:
- Context is provided
- The model processes it
- A step completes
- The next step restarts from the beginning
Each step pays the full cost of understanding the same information.
Depth equals repeated work.
A Different Execution Model
Now imagine a workflow where:
- Shared context is processed once
- Subsequent steps build on that execution state
- Only new information is incrementally added
Nothing about agent logic changes. Nothing about outputs changes.
But the cost profile does.
What Actually Improves
When context isn't reprocessed every step:
- Verification becomes cheap enough to keep on
- Latency stabilizes instead of compounding
- Workflow depth stops being a budget constraint
The system doesn't feel faster because tokens generate quicker. It feels faster because it stops doing unnecessary work.
Why This Is Structurally Different From Caching
This isn't about saving identical requests.
Multi-step workflows aren't identical. They evolve.
The improvement comes from execution continuity, not request reuse.
That distinction is why existing optimizations plateau.
Why This Matters Operationally
Once teams experience this shift:
- They stop designing workflows around cost avoidance
- They stop removing safeguards for budget reasons
- They stop treating depth as a liability
Execution becomes something you optimize once—not something you pay for repeatedly.
Understanding LLM prefill cost reveals why reprocessing dominates. The difference between inference optimization and execution efficiency becomes clear when you see how context window cost scales with depth.
CLC Labs focuses on this execution shift—without requiring changes to agent logic or models.