Build a control layer for AI agents that allocates and enforces budgets for model usage, tool calls, execution time, retries, and external service costs.
Allow developers to define agent workflows, task priorities, per-step budgets, global limits, and minimum quality requirements. Estimate costs before execution when pricing information is available, then track actual usage during execution.
Create policies for choosing cheaper models, shortening context, caching repeatable results, limiting retries, asking for human input, or stopping a task when its remaining budget cannot support completion.
Provide a live execution trace showing budget consumption, remaining resources, quality checks, and the reasons behind routing decisions. Include simulation mode for comparing alternative budget policies against the same workload.
Support hard limits, explicit approval thresholds, and safe termination behavior. Distinguish estimated costs from actual billed amounts, keep provider pricing configurable, and never silently trade away required accuracy or security to meet a budget.
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