Budget & cost control for AI agents: hard per-agent spend caps, rate limits,
idempotency, and human-in-the-loop approval — enforced before each LLM call, not
after the invoice. One hosted MCP endpoint (no proxy or self-hosting), settled
via x402 (USDC on Base).
Enables agents to track real-time token spend and cost velocity, automatically throttle or freeze execution when budgets are exceeded, and enforce sandbox and safety guardrails.
Enables AI agents to monitor live token consumption and rate-limit windows across providers and assess whether expensive actions are justified before exhausting quotas, with optional budget guardrails.
Governs AI agents' spending by enforcing budgets, approvals, and kill switches before any payment, providing an MCP interface for tool calls with policy checks.