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AI Agent Multi-Step Reliability Calculator

A workflow that is 95 percent reliable per step is only about 60 percent reliable over ten steps. This calculator shows the compounding and how much retries and shorter chains help.

Your numbers

Per-step success rate

Chance a single tool call plus decision is correct and usable.

Steps in the workflow

10 steps

Retries allowed per step

Tasks per month

Minutes to fix a failed task by hand

minutes

Your results

End-to-end success with retries

97.5%

Manual fix effort per month

12 hrs

Per-step success as a fraction

0.95

Per-step success with retries

99.8%

End-to-end success without retries

59.9%

Expected tool calls per task

10.5

Assumes independent steps and that a retry can fix a failure. Systematic errors, such as a wrong tool description, are not helped by retries.

Save results:

End-to-end success as steps increase

Sweeps Steps in the workflow from 1 to 40, holding your other inputs fixed.

Agent evaluation help

Want a step-level evaluation set built from your ERP tasks? Optional follow-up.

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

How to use it

Estimate per-step success from your evaluation set: the share of single tool calls that produce a correct, usable result. Set the number of steps a typical task needs and how many retries each step is allowed. The tool reports end-to-end success with and without retries, expected tool calls and the monthly hours of human cleanup implied by the failure rate. The chart plots both curves across one to forty steps so you can see how fast a long chain decays.

Formula

Without retries, success is p to the power n. With r retries, a step fails only if all r plus 1 attempts fail, so step success is 1 minus (1 minus p) to the power r plus 1, and the chain is that value to the power n. Expected attempts per step is step success divided by p, which is how the calls output is derived. The model assumes failures are independent and detectable, so the agent knows when to retry.

Cutting steps usually beats adding retries.
Validate outputs so failures are detected, not silent.
Human approval on writes caps the cost of a silent error.

Reading the result

If end-to-end success is below what your process tolerates, either raise per-step reliability with clearer tool descriptions and constrained arguments, or shorten the chain by merging steps into a single deterministic tool. For ERP tasks, put the fragile reasoning in as few steps as possible and let code do the lookups and joins. Measure on real tasks, since step reliability varies sharply by tool.

Frequently Asked Questions

Why is end-to-end success so much lower?

Because every step must succeed. Probabilities multiply, so a small per-step failure rate compounds. Ten steps at 95 percent give about 60 percent, and twenty steps give about 36 percent.

Do retries always help?

Only for random failures that a second attempt can fix and that the agent can detect. If the same wrong tool or bad argument is chosen each time, retries repeat the error and only add cost.

How do I measure per-step success?

Build a labeled set of single-step cases from real ERP queries and actions, run the agent and score each result against the expected one. Track the rate by tool, since a few tools usually cause most failures.

What is an acceptable end-to-end rate?

It depends on the cost of failure. For read-only lookups with easy verification, lower rates may be fine. For writes to financial or inventory records, aim for human approval regardless of the figure.

Netray designs agents with deterministic ERP tools, validation and human approval on write-back. We can help you test per-step reliability on your own data.

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