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# The 3x Rule of Agent Costs ​

June 10, 2026

Here is a rule that will save you from budget surprises: production agent costs 3x your prototype estimate.

## Why the Multiplier Exists ​

Phase | Multiplier | What Happens
--- | --- | ---
Prototype | 1x | Happy path works perfectly
With retries | 1.5x | Failed tool calls retry, edge cases handled
Production | 3x | Monitoring, error handling, observability, security

## Where the Cost Goes ​

Output tokens dominate — about 70% of total cost. The model's reasoning is the expensive part. Input tokens (context) are about 20%. Cached tokens are 10%.

Every tool call costs 3-5x more than the call itself:

- Planning which tool to use
- Executing the tool
- Error recovery if it fails
- Parsing the result
- Adding the result back to context

## Quick Estimation ​

python

    def estimate_cost(turns, tokens_per_turn, price_per_m):
        base = turns * tokens_per_turn * price_per_m / 1_000_000
        return {
            "prototype": base,
            "with_retries": base * 1.5,
            "production": base * 3.0
        }

## Budget Accordingly ​

If your prototype agent costs $0.10 per task, plan for $0.30 in production. At 10,000 tasks per month, that is $3,000 per month, not $1,000. The 3x rule keeps you honest.

From Module 6 of the Agentic Engineering Course. The full module covers cost optimization, cascade routing, and pass@k evaluation.