Token Economics in Azure: Optimizing AI Agent Costs with Model Router and Agent Optimizer

Token economics for AI applications is framed around how total token usage and model selection drive the cost of running agents in production. The discussion explains why AI workloads are non-deterministic, why prototype defaults can overpay for routine tasks, and how a managed investment approach can balance cost and quality. Microsoft Foundry capabilities are demonstrated as practical controls for selecting models at runtime and improving agents over time.

■ Cost drivers and production challenges
- Total token cost as tokens multiplied by price per token
- Stateless model calls carrying full context and agent orchestration loops
- Non-deterministic AI behavior and prototype-to-production mismatches

■ Microsoft Foundry optimization capabilities
- Model Router: runtime model selection by quality, cost, or balanced mode with model subsets and reliability routing
- Agent Optimizer: evaluation and optimization using production traces, prompt/tool/model tuning, and estimated optimization cost
- Cost optimization preview for lower spend

■ Operating model and next steps
- Running AI as a managed investment with request-level and post-production optimization
- Guardrails and limits to avoid run-off costs
- Deploy Model Router alongside an existing endpoint, compare evaluations, and run Agent Optimizer

AI developers, platform teams, and FinOps stakeholders evaluating Azure AI deployments will find a structured view of how to reason about agent costs and where Foundry tooling fits. Viewers can leave with a clearer checklist for budget reviews and a small first step for testing model routing and agent optimization in their own environments.