Every dollar attributed to the task that spent it, rolled up to the work order, the phase, the role, and the model. Frontier-model share is a KPI, not a year-end finding.
Three loops that bend the cost curve down and the capability curve up at the same time. None of them require retraining.
Every expensive explanation is captured once and reused forever. As the memory layer fills, local models absorb a growing share of routine work, and frontier models get reserved for high-leverage reasoning.
Work arrives with its own evidence packet. Reviewers verify the gates and spot-check the diff instead of re-reading every line, so throughput per engineer compounds.
Decisions, approved patterns, and failure modes accumulate. Tomorrow's agents inherit today's lessons, and that knowledge survives engineer turnover.
Spend is a property of a task, not a line on a vendor invoice. Every dollar is attributed to the unit of work that spent it, all the way up the work graph.
Cheap local models handle the high-volume, class-bounded work. Frontier models handle the high-leverage, decision-class reasoning. AgentOS routes by task class and cost class — never by whoever shouted loudest.
Most of the work is routine: grounding lookups, embeddings, classification, summarization, formatting. Local models on your own hardware do this work at near-zero marginal cost.
When a task requires open reasoning, a frontier model is the right tool. AgentOS reserves the lane for that, so spend goes where leverage is.
The contract that authorizes a task also bounds it. Budget is a required field, fail-closed when exhausted, and visible in real time.
Token usage and model rates roll up per task in near-real time. No batch reconciliation, no surprise overruns at month-end.
A task that runs out of budget halts at the boundary and surfaces. It does not silently borrow from the next task, the next phase, or the next month.
Frontier share is a tracked number. If it climbs, that is a signal something is escaping the routing rules, not a quarterly surprise.
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