Token-cost visibility and optimizer for multi-agent AI orchestration

As teams compose modular multi-agent workflows, token spend and cross-call complexity create operational blind spots—this is acute for internal platform teams and roughly 300,000 AI development teams worldwide that are building production AI and facing rising model costs. High-frequency calls and large-context models have made tokens a first-order expense, and most teams lack tooling to attribute spend, predict costs, or automatically tune pipelines for cost/accuracy trade-offs. You could build an agent-facing SDK plus a central FinOps dashboard and policy engine that provides per-agent and per-call token attribution, cost forecasting across providers, and an online optimizer that recommends or enforces cost/accuracy adjustments; integrations with major LLMs and orchestration frameworks would be essential. With a $9.6B TAM (300k teams × $32k ACV), a market score of 88/100, and clear buyers in platform and FinOps teams, demand is present now as multi-agent patterns and platformization of AI accelerate. The core commercial promise is measurable: conservative 10–30% token spend improvements would justify adoption quickly and support the stated revenue potential (82/100). To differentiate in a medium-competition field you must offer deep instrumentation into orchestration flows, flexible per-provider pricing models, and an optimizer that can simulate trade-offs without degrading production accuracy; open-source connectors and enterprise-grade controls will speed enterprise buy-in. The key challenges are maintaining adapters to shifting provider APIs and pricing, proving consistent ROI across heterogeneous workflows, and earning placement as a control plane, but if you can demonstrate reproducible savings on real workloads this becomes a straightforward platform purchase.