When you run one agent, you pick a framework and ship it. When you run dozens, you face a different problem: each team chose a different stack, each agent calls different models, and now you need unified observability, authentication, and deployment without rewriting everything to match a single vendor's API.
AWS published this as part two of a multi-agent series, addressing the operational reality ML teams hit after proof-of-concept. The focus is not on building a single agent. It is on operating a fleet of heterogeneous systems without creating hard dependencies on any one framework, model provider, or cloud service.
The Multi-Everything Problem
Enterprise ML teams end up with:
Multiple frameworks: LangChain for prototyping, CrewAI for multi-agent workflows, custom orchestrators for specialized tasks
