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AI Agent Error Cost Break-Even Calculator

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.

Your numbers

Actions per month

actions

Agent error rate

2 %

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

Human baseline error rate

Measure it, do not assume humans are perfect.

Agent errors caught before impact

Approval gates, validations and downstream checks.

Human errors caught before impact

Loss per escaped error

Rework, expedite, credit notes, write-offs, penalty exposure.

Cost per caught error

Time to fix an error that was caught in time.

Your results

Error cost advantage of agent

$10,625

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

Break-even agent error rate

4.1%

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

Agent expected monthly loss

$10,000

Human expected monthly loss

$20,625

Agent errors escaping per month

Loss per wrong agent action

$100

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

Save results:

Expected monthly error loss vs agent error rate

Sweeps Agent error rate from 0 to 15, holding your other inputs fixed.

Get a second opinion on your risk model

Optional: share your error and loss assumptions and we will review them.

Optional. The tool and every export work without this. No spam.

How to use it

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

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.

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

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.

Frequently Asked Questions

Where do I get the human error rate?

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?

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?

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?

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

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.

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