How to build Microsoft AI agent framework effectively

When people talk about cost in Microsoft Agent Framework workloads, the first suggestion is almost always "use a cheaper model." That advice isnt wrong, but it also misses where a lot of the spend actually comes from once you start running real multi-agent systems. In many cases, the bigger cost leaks come from context being passed around repeatedly between agents, workflows getting retried or resumed, large retrieval payloads, verbose logs, structured outputs, and expensive models being used for tasks that really dont need that level of reasoning. A pattern I've found much more effective is keeping Microsoft Agent Framework as the orchestration layer and putting a gateway underneath it. The gateway becomes responsible for things like caching, context compression, and model routing, so the workflow can make smarter decisions about what actually needs a premium model. Think about a support triage flow. A ticket comes in, gets classified, summarised, matched against documentation and previous fixes, then a draft response is generated. Only when the case is genuinely ambiguous does it get escalated to a stronger model. Most of that workflow shouldnt be billed as if its doing deep reasoning. The real optimisation opportunity is not just model selection, its making sure each step gets the level of intelligence it actually needs. Thats usually where the meaningful cost savings show up.