Most teams underestimate AI agent cost for the same reason:
They model requests instead of execution depth.
Retries compound across steps.
Memory grows between tool calls.
A single task becomes many model invocations.
The Scenario: AI Agent (Production Workflow)
Imagine a tool-using AI agent embedded into a product workflow.
It plans.
It calls tools.
It retries failures.
It reflects before responding.
Assumptions
- ~1,500 tasks per day
- 3–5 reasoning steps per task
- Tool calls introduce retry risk
- Memory accumulates across steps
This already rules out “cost per request” thinking.
Because there isn’t one request.
There’s execution depth.
Step 1 — Start With Depth, Not Volume
Traffic matters — but only after you understand step multiplication.
The real cost driver appears when:
- A task expands from 3 steps to 6
- Tool retries compound across steps
- Context grows silently between calls
AI agents don’t spike like traffic systems.
They amplify.
Step 2 — Define a Planning Baseline
In ModelIndex, this is the Expected scenario.
Expected means:
- Controlled execution depth (3–5 steps)
- Retry rate under 10%
- Memory trimmed between tasks
It is not optimistic.
It is not worst-case.
It’s what a disciplined production agent looks like.
Step 3 — Identify the Amplification Boundary
Now examine what happens when depth expands.
This is not about model choice.
It’s about structure.
When:
- Average steps exceed ~6
- Retries stack across those steps
- Reflection loops extend execution
Cost stops behaving proportionally.
It compounds.
Worst-case is not a crash.
It’s where execution becomes unbounded.
Step 4 — Ask the Right Question
The useful question is not:
Which model is best for agents?
It is:
What happens when execution depth expands beyond control?
That’s the insight teams usually discover after production incidents.
Step 5 — Make an Intentional Decision
With this view, teams typically choose to:
- Cap step depth
- Add retry guardrails
- Limit reflection cycles
- Ship with tighter constraints
The important part is not the restriction.
It’s that the system behaves predictably.
Why This Matters
AI agent costs don’t surprise teams because models are expensive.
They surprise teams because execution depth was never modeled.
ModelIndex exists to make that structure visible before launch.