Stop agent tool loops from blowing API budgets via cost-aware orchestration

Teams building production AI agents increasingly face runaway API spend when agents enter tool-calling loops or make many speculative calls; finance and engineering managers at mid-to-large SaaS and platform companies see these surprises as operational and budgetary risks. With an estimated addressable market of 500,000 AI app teams and a $6.0B opportunity (500K × $12K ACV), unpredictable agent-driven costs are a common and solvable pain point. You could build a cost-aware orchestration layer that sits between planners and tools to enforce per-agent budgets, estimate and simulate call costs, and apply runtime policies like throttling, batching, and graceful fallbacks when budgets approach limits. Core capabilities would include lightweight SDKs for popular agent frameworks, real-time cost telemetry and alerts, predictive cost modeling, and policy-as-code with audit trails to support chargeback and compliance workflows. The market is attractive now because agent adoption is accelerating and tool-calling is driving direct, metered API usage that finance teams want to control, while runtime observability is maturing enough to make enforcement feasible. The opportunity scores well (market score 88/100, revenue potential 82/100) and aligns with trends toward cost sensitivity and production-grade instrumentation. To stand out you should prioritize non‑invasive, vendor‑neutral integrations, high-accuracy per-call cost prediction, and UX-preserving controls (fast-fail, graceful degradation) rather than just analytics dashboards. Be honest about challenges: integrating across heterogeneous tool ecosystems, handling variable or opaque API pricing, and getting engineering teams to accept added control in latency‑sensitive paths will require careful product and go-to-market work.