The Economics of Agent Optimization: Four ways to lower the cost

AI agents are becoming central to enterprise automation, but their operational costs can spiral quickly. Every API call, token processed, and model invocation adds up—especially when agents run at scale. Microsoft’s recent exploration of agent optimization through Azure Foundry highlights a practical reality: you don’t always need to rewrite your agent logic to reduce expenses. Instead, there are four systematic levers you can pull that apply to every request, before your agent even executes a single line of code.

Understanding the Four Levers

The optimization approach focuses on factors that operate at the infrastructure level. First is model selection—choosing the right-sized model for each task rather than defaulting to your most capable (and most expensive) option. Second involves request-level caching, which captures responses to identical or similar queries and serves them without re-processing. Third is batching and scheduling, where you group requests intelligently to improve throughput and reduce per-request overhead. Fourth centers on prompt optimization—trimming unnecessary context, using structured outputs, and reducing token consumption without sacrificing accuracy.

Why this matters practically: imagine running a customer support agent across 10,000 daily inquiries. If you’re currently using your largest model for every request and regenerating answers to frequently asked questions, switching to a smaller model for simple queries and caching common responses could cut your infrastructure costs by 40-60%. For a mid-size operation running agents continuously, that translates to meaningful savings on your Azure bill. These levers work independently and together—you might apply model selection to 70% of requests, caching to 80%, and prompt trimming across everything, creating a compounding efficiency gain.

Putting It Into Practice

The real power emerges when you treat optimization as a data-driven exercise. Start by instrumenting your agents to track which requests consume the most tokens and compute. Look for patterns: Are certain user queries consistently simple? Are you sending the same context repeatedly? Build A/B tests comparing a standard agent against an optimized version. The Microsoft approach suggests this is easier than you might think—these levers sit in your infrastructure layer, meaning you can experiment without refactoring your agent code. For teams already managing agents on AWS (using Bedrock or SageMaker) or self-hosted models, the same principles apply: profile your costs, identify waste, and apply targeted optimizations systematically rather than hoping architectural rewrites will solve the problem.