Task Graph Topology
A source simulation step has independent per-attempt reliability point eight two and two retries, meaning up to three attempts. Under that assumption, success probability is one minus point one eight cubed, or point nine nine four one six eight. Bars span one hundred eighty point four and two hundred eighteen point seven two at two hundred twenty pixels per probability. Latency for its base two point five seconds grows by twenty percent on each successive attempt: two point five, three and three point five, totaling nine if all three are tried. This is an illustrative mathematical retry model, not measured provider reliability. Earlier step failures halt each simulated workflow, so later step failure frequencies also depend on how often those steps are reached. The retention captions are a separate authored rubric rather than observed cohorts. For an explicit hypothetical perfect success rate, mean latency ten seconds and one human-in-loop step, latency contributes twenty friction points and the human count contributes five, giving twenty five. Day one is capped at point nine eight. Day seven subtracts friction divided by two hundred fifty, yielding point eight eight. Day thirty subtracts friction divided by two hundred, yielding point seven five five. Bars span one hundred ninety six and one hundred fifty one at two hundred pixels per retention fraction. Even perfect task success therefore has attrition by construction. This does not establish real user retention or the causal effect of human review. The run button simulates one thousand random workflows using the supplied probabilities, then writes success, friction and retention captions before attempting its D3 chart. Missing D3 initially prevents graph rendering and inspector updates, but listeners were already installed. Running the button can still compute local metrics and create a state that JSON exports, while its chart call throws. Bars show two hundred pixels for one thousand synthetic runs at point two per run and zero actually observed user cohorts. Native reliability controls can mutate a selected step before their graph redraw fails; a subsequent run reads that changed step. Paired offline tests use a shared random seed only in test contexts to compare originals fairly. They do not modify public randomness or turn simulated scores into measured retention.