AI Agent Reliability & Failure Cascade Simulator
Model how dependent agent steps, retries, fallbacks and tool expenses affect end-to-end success and cost.
← Explore the full tool libraryEnter your scenario
The form starts with hypothetical examples. Replace them with your own measurements or assumptions. Inputs and exports stay in this browser.
What decision does this help answer?
If three agent steps each succeed most of the time, how often does the whole workflow finish successfully?
Calculation and methodology
For each sequential step, assume independent attempt success probability p and up to k tries; step success is 1 − (1 − p)^k. A separately configured recovery path may handle an exhausted step. Multiply adjusted step-success probabilities to obtain a simplified workflow success estimate; compute expected attempt counts from geometric truncation and estimate the weighted cost while accounting for workflows that stop early.
Limitations and responsible interpretation
Independence, identical retry odds and fixed per-attempt expenses rarely hold perfectly in production. Correlated failure modes, idempotency, external side effects and tail latencies demand real tracing. Outputs are assumptions-based simulation results, not uptime guarantees.
Research and supporting guidance
This planning tool complements Ninth Post's related AI product analysis. Consult the underlying technical guidance or official framework where applicable, and see our editorial methods. The tool does not independently validate submitted assumptions, current vendor prices or legal obligations.