# AI Agent Error Cost Break-Even Calculator

> Source: https://netray.co/tools/ai-agent-error-cost-break-even-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Find the error rate at which an AI agent costs more in mistakes than your current human process, given catch rates and loss per escaped error.

## Category

ai-automation

## Audience

- CIO
- IT manager
- Risk and compliance

## Tool

**Kind:** calculator

**Currency:** $

**Inputs:**

#### Actions per month

**Key:** actions

**Type:** number

**Default:** 5000

**Min:** 0

**Step:** 100

**Unit:** actions

#### Agent error rate

**Key:** agent_err

**Type:** slider

**Default:** 2

**Min:** 0

**Max:** 15

**Step:** 0.25

**Unit:** %

**Help:** Share of actions that are wrong, measured on a validation sample.

#### Human baseline error rate

**Key:** human_err

**Type:** number

**Default:** 3

**Min:** 0

**Max:** 100

**Step:** 0.25

**Unit:** %

**Help:** Measure it, do not assume humans are perfect.

#### Agent errors caught before impact

**Key:** agent_catch

**Type:** number

**Default:** 80

**Min:** 0

**Max:** 100

**Step:** 1

**Unit:** %

**Help:** Approval gates, validations and downstream checks.

#### Human errors caught before impact

**Key:** human_catch

**Type:** number

**Default:** 70

**Min:** 0

**Max:** 100

**Step:** 1

**Unit:** %

#### Loss per escaped error

**Key:** escaped_loss

**Type:** number

**Default:** 400

**Min:** 0

**Step:** 25

**Unit:** $

**Help:** Rework, expedite, credit notes, write-offs, penalty exposure.

#### Cost per caught error

**Key:** caught_cost

**Type:** number

**Default:** 25

**Min:** 0

**Step:** 5

**Unit:** $

**Help:** Time to fix an error that was caught in time.

**Outputs:**

#### Agent expected monthly loss

**Key:** agent_loss

**Expr:** actions * agent_err / 100 * ((1 - agent_catch / 100) * escaped_loss + agent_catch / 100 * caught_cost)

**Format:** currency

#### Human expected monthly loss

**Key:** human_loss

**Expr:** actions * human_err / 100 * ((1 - human_catch / 100) * escaped_loss + human_catch / 100 * caught_cost)

**Format:** currency

#### Error cost advantage of agent

**Key:** delta

**Expr:** human_loss - agent_loss

**Format:** currency

**Primary:** true

**Description:** Positive means the agent's errors cost less than your humans' errors.

#### Agent errors escaping per month

**Key:** escaped_agent

**Expr:** actions * agent_err / 100 * (1 - agent_catch / 100)

**Format:** decimal

#### Loss per wrong agent action

**Key:** unit_agent

**Expr:** (1 - agent_catch / 100) * escaped_loss + agent_catch / 100 * caught_cost

**Format:** currency

#### Break-even agent error rate

**Key:** breakeven

**Expr:** if(gt(unit_agent, 0), human_loss / (actions * unit_agent) * 100, 0)

**Format:** percent

**Primary:** true

**Description:** Above this error rate the agent costs more in mistakes than the human process.

**Chart:**

#### Expected monthly error loss vs agent error rate

**Type:** line

**X:** agent_err

**From:** 0

**To:** 15

**Steps:** 30

**Series:**

- agent_loss
- human_loss

**Disclaimer:** Expected value model. It ignores rare catastrophic events, regulatory penalties and reputational damage, which need separate treatment.

## Content

**Intro:** Whether an AI agent is good enough is not a question about accuracy alone: it depends on how many errors are caught and what an escaped error costs. This tool compares expected error loss for the agent and your human baseline and finds the break-even error rate.

**Sections:**

#### How to use it

**Body:** Start with your measured human error rate, not a guess. Pull a month of corrected purchase orders, credit notes or rework tickets to estimate it. Enter the agent error rate from a validation sample, then set catch rates for each path: an approval step, three-way match, or a downstream report may catch errors before they reach a supplier or customer. Set the loss per escaped error from finance data. The chart shows where the two lines cross.

#### The math

**Body:** Loss per wrong action = (1 - catch rate) x escaped loss + catch rate x cost to fix a caught error. Expected loss = actions x error rate x loss per wrong action. Break-even error rate = human expected loss / (actions x agent loss per wrong action). Because the catch rates differ, an agent with a higher raw error rate can still win if its errors are visible and gated.

**Bullets:**

- Gated write-back raises the catch rate.
- Escaped loss dominates when it is large.
- Break-even above 100% means the agent cannot lose on this input.

#### Common mistakes

**Body:** Comparing the agent to a perfect process. Treating all errors as equal: a wrong item description is not a wrong bank account. Using vendor-quoted accuracy rather than a sample from your own data. Splitting workflows by severity is better than one blended number, so run the calculator once per risk class. Re-run it quarterly with measured numbers, because both error and catch rates drift as data and prompts change.

**Cta:** Netray helps teams measure agent error rates on their own ERP data and design the gates that raise catch rates. We can help set up the validation sample.

**Faq:**

#### Where do I get the human error rate?

**Answer:** Sample closed transactions, count corrections, credit notes and rework tickets, and divide by volume. Even a rough two-week sample is better than an assumption.

#### Can break-even be higher than 100 percent?

**Answer:** Yes, it can come out very large when the agent's loss per wrong action is tiny relative to the human loss. Treat any value above the plausible range as 'agent cannot lose' on this input.

#### Does this cover rare large losses?

**Answer:** No. Expected value hides tail risk. For payments, compliance filings or export-controlled data, keep a human gate regardless of the expected-value result.

#### How often should I update inputs?

**Answer:** Quarterly, or after any model, prompt or ERP data change. Error and catch rates both drift.

## Search terms

ai agent error rate break even, cost of ai mistakes calculator, expected loss ai automation, ai vs human error cost, erp agent error cost, ai risk expected loss calculator, acceptable ai error rate

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