Introduction
Your multi-agent customer service platform handles millions of daily interactions across 14 specialized agents—but the AI infrastructure costs are running 40% over budget. You need answers. The problem isn't simple: some agents process thousands of routine queries that don't need expensive models, while others handle complex policy interpretations that require advanced reasoning. Some customer interactions involve long multi-turn conversations where context grows geometrically, while others are single-turn lookups. Optimizing a single agent is straightforward; optimizing a distributed system of 14 interdependent agents at production scale requires a different approach.
In this module, you learn how to:
- Design intelligent model routing strategies that match task complexity to the appropriate model tier
- Implement multi-level caching that reduces repeated inference costs without sacrificing quality
- Optimize token usage across long multi-agent conversations through context management
- Balance quality-cost-latency tradeoffs for different customer segments
By the end of this module, you're able to optimize a distributed multi-agent system for cost, latency, and quality—without sacrificing the customer experience that justifies the investment.