Part of a series on execution-layer efficiency in multi-step AI systems.
Most teams think they understand inference costs.
You send a prompt. The model responds. You pay for tokens and latency.
That mental model holds—until you build real agentic systems.
Once workflows move beyond a single call into planner → executor → verifier, costs don't just rise. They compound.
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.
Understanding why **LLM inference cost** scales with workflow depth is essential for infrastructure teams building production AI systems.
The Single-Call Illusion
Single-turn inference is clean and intuitive:
- Encode the prompt
- Run attention
- Generate tokens
- Done
Costs scale roughly with:
- Prompt length
- Model size
- Output length
This is the world most benchmarks describe. It's also not how production AI actually runs.
What Breaks in Multi-Step Workflows
Modern AI systems don't reason once. They reason in stages.
A typical workflow:
- Read a large document
- Form a plan
- Execute subtasks
- Verify results
- Refine or retry
Each step depends on the same shared context.
And here's the problem:
Every step re-encodes that context from scratch.
The Hidden Cost of Reprocessing
Imagine a workflow with:
- 10 steps
- A shared 50,000-token context
Naively executed, this looks like:
- Step 1: process 50,000 tokens
- Step 2: process 50,000 tokens again
- Step 3: process 50,000 tokens again
- …
- Step 10: process 50,000 tokens again
Nothing about the context changed—but the model paid the full compute cost every time.
Depth × Context = Cost Explosion
The system performs the same expensive work repeatedly, even though the information is identical.
Why Value Doesn't Scale With Cost
Here's the mismatch most teams eventually hit:
- Early steps add real value (understanding, framing, reasoning)
- Later steps mostly refine, verify, or validate
The marginal value per step declines, but the compute cost does not.
Your tenth step costs as much as your first—even though it delivers far less incremental insight.
This creates a structural inefficiency:
- Cost scales linearly with depth
- Value scales sub-linearly
What Teams Do in Response (And Why It's a Problem)
In production, teams adapt—but not in good ways:
**They cap workflow depth** Not because deeper reasoning isn't useful, but because it's too expensive.
**They disable verification** Reflection and checking double inference cost, so they get turned off. This is why AI verification loops get disabled in production.
**They self-host prematurely** Not to improve quality—but to survive the economics of repeated inference. Teams eventually move to self-hosted LLM inference to regain control.
**They chase micro-optimizations** Faster models, better batching, cheaper tokens—none of which fix the core issue.
These are symptoms. Not solutions.
This Isn't a Tooling Problem
The problem isn't:
- Prompt engineering
- Agent frameworks
- Faster inference kernels
Those optimizations help individual calls.
They do nothing about redundant execution across steps.
As models get:
- Larger
- More capable
- More expensive
And as workflows get:
- Deeper
- More agent-driven
- More context-heavy
Execution efficiency—not model quality—becomes the limiting factor. Understanding the difference between inference optimization and execution efficiency is critical.
The Structural Shift That Has to Happen
The root issue is simple:
We treat every step as stateless, even when the state hasn't changed.
Modern AI systems repeatedly re-encode the same information because there is no standard way to continue execution from prior internal state.
Until that changes:
- Deep workflows will remain fragile
- Verification will remain optional
- Costs will scale faster than value
The Inevitable Infrastructure Shift
This is an execution problem. As agentic workflows move into production, repeated context processing becomes a structural constraint that grows with workflow depth. This creates pressure for new infrastructure layers that address execution efficiency, not just inference speed.
The root issue is that multi-step workflows treat every step as stateless, even when the state hasn't changed. Modern AI systems repeatedly re-encode the same information because there is no standard way to continue execution from prior execution work.
Until execution-layer infrastructure addresses this, deep workflows will remain economically constrained, verification will remain optional, and costs will scale faster than value delivered.
CLC Labs is working with early teams exploring these constraints in real systems.