Multi-step agent reliability calculators
An agent built as a chain fails whenever one step does. Size end-to-end success under independent and shared failures, price retries, set a human-review cutoff, and test prompt robustness.
What these numbers mean
Ten steps at 95% each finish the whole job about 60% of the time (0.95^10 = 0.60). Every step can look shippable on its own and the whole run still fails far more often than the per-step numbers suggest. These five calculators take one piece each: what the chain delivers today and what each fix would buy.
| What you have | The instrument |
|---|---|
| A chain of steps, each with its own success rate | compound the whole chain |
| Steps that tend to fail together on the same input | compound it under shared failures |
| A retry or fallback you might pay for | price the retry policy |
| Confidence scores and a cost for human review | set the human-review cutoff |
| The same eval rerun on a reworded prompt | measure prompt robustness |
| A chain that passes today, and a release decision riding on whether it still will next time | test it as a release regression |
The arithmetic and the failure budget come from our own analyses: budgeting for failure in production agents sets up the chain view, how far one failure reaches through an agent system covers shared causes, and measuring agent reliability past a single pass rate with the checklist a green eval skips covers where the input numbers come from.
The choice that trips people up is whether the steps fail independently. If one bad input, a rate limit, or a flaky tool can knock out several steps at once, the independent-failure math is the wrong model for your chain. The correlated simulator prints both figures side by side with the gap between them, so run it whenever a shared cause is plausible.
Tools in this topic
Multi-Step Agent Reliability Calculator (Independent Steps)
Compute the end-to-end success rate of a multi-step agent chain from its per-step reliability, and the per-step budget your target reliability demands.
Instrument | Multi-step agent reliabilityHuman Review Threshold Optimizer for Confidence-Gated Automation
Derive the human review threshold from your own records: paste stated confidence and observed correctness, price one review against one missed failure, and read the cut that costs least.
Instrument | Multi-step agent reliabilityRetry, Verification, and Fallback Reliability Optimizer for LLM Agents
How many times should you retry a failed agent step? Compare retry, majority vote, verifier gate, fallback, and escalation on delivered correctness and expected cost per task, from your own rates.
Instrument | Multi-step agent reliabilityPrompt Robustness Analyzer: Perturbation Stability for LLM Evals
Is a prompt's pass-rate drop real or noise? Paste pass/fail results for a baseline prompt and its semantically equivalent variants and read each change with a paired interval and an exact McNemar p.
Instrument | Multi-step agent reliabilityMulti-Step Agent Reliability with Shared Failures (Cascade Simulator)
Price a multi-step agent chain when its steps share a cause: end-to-end reliability under common-cause failure beside the independent figure, plus the retry ceiling that shared failures impose.
Analyses that use these calculators
- Judge reliability
Kappa thresholds for LLM judges, and who published each one
Five published kappa bands from four sources, side by side, each with the author who wrote it, the date we read it, and the coefficient it was written for. They are conventions, and they disagree.
- RAG & retrieval
Agentic RAG architecture, and where each pattern breaks
Agentic RAG wraps retrieval in an autonomous agent that plans, routes, and self-corrects. Here is the architecture, the failure modes each pattern adds, and when classic RAG is the safer call.
- Reliability testing
The agent reliability testing checklist your green eval skips
A pre-flight agent reliability testing checklist: six checks with pass criteria and calculators, from failure-mode coverage and statistical power to pass^k, cascade budget, and judge calibration.
- Reliability testing
Make AI agents reliable in production by budgeting for failure
Reliable AI agents in production start with the compounding math: 0.95 per step over 20 steps is ~36% end to end. A topology-aware playbook of levers: retry, fallback, checkpoint, human-gate, degrade.
- RAG & retrieval
RAG pipeline failure modes, and the gate that stops each one
RAG pipeline failure modes are containment failures at the retrieval-to-generation boundary: five modes mapped to how each propagates, its detection signal, and the gate that holds it.