Enterprise AI Agent Orchestrator

The saved deployment architect assigns agent nodes fixed token prices and base latency values. Using its stored mini-model rates, one thousand input tokens cost fifteen hundredths of a cent and one hundred output tokens cost six hundredths of a cent, totaling point zero zero zero two one dollars per query. At ten thousand daily queries and thirty days, that gives sixty three dollars. Doubling input to two thousand while keeping output one hundred gives point zero zero zero three six dollars per query and one hundred eight dollars monthly. Bars compare one hundred twenty six and two hundred sixteen pixels at two pixels per monthly dollar. These are exact calculations from saved fixture rates, not verified current pricing. Other nodes contribute a fixed cost per call. Every node in the inventory is included regardless of whether graph edges make it reachable from the query path. The estimate does not invoke any model or deploy infrastructure. For an agent node the latency estimate equals stored base latency plus rounded fifteen milliseconds per one hundred output tokens. A mini-model base of one hundred eighty milliseconds and output one hundred gives one hundred ninety five milliseconds. Two such nodes contribute three hundred ninety milliseconds because the metric sums every node. Bars show two hundred seventy three and one hundred thirty six point five pixels at point seven pixels per millisecond. This is not a graph critical path calculation. Parallel branches, unreachable nodes, cycles and conditional execution are not resolved by that summation. The bottleneck label is the node with greatest individual latency, with the first maximum retained on ties. Tool nodes use their latency or a twenty millisecond fallback, so a zero latency also falls back. The animated workflow traverses the node array, rather than scheduling graph dependencies, and uses base latency without the output adjustment. Its visual trace does not prove actual service execution. The SLA label is assigned ninety nine point four percent whenever summed latency is at most one thousand milliseconds. Above one thousand, it subtracts the excess divided by twenty, with a floor of seventy. At fourteen hundred milliseconds the assigned value is seventy nine point four percent. Bars compare two hundred ninety eight point two and two hundred thirty eight point two pixels at three pixels per assigned percentage point. No observed uptime, latency distribution or percentile supports those labels. Budget utilization divides monthly cost by configured budget; status switches above eighty and above one hundred percent. Falsy daily volume falls back to ten thousand and a falsy monthly budget falls back to fifteen hundred, while the budget input handler itself replaces zero with one thousand. The JSON manifest exports current configuration rather than measured results. This offline copy lacks Cytoscape, so graph initialization interrupts preset setup before later metric updates. The explainer describes saved estimation logic, without claiming a healthy deployment or real reliability guarantee.