Scaling agentic AI: Enterprise patterns without vendor lock-in | Amazon Web Services
Scaling agentic AI across an enterprise requires architectural patterns that preserve flexibility while avoiding vendor lock-in. This post is Part 2 of our series on multi-agent systems at scale. In this post, we examine how machine learning (ML) teams operate agentic AI systems across a “multi-everything” environment of frameworks, models, and providers. We also cover the principles that let those systems scale together.
In Advanced fine-tuning techniques for multi-agent orchestration patterns from Amazon at scale, we explored how to design and optimize multi-agent orchestration within a single use case or domain. That post focused on scenarios where multiple agents are required to handle complexity by coordinating workflows, decomposing tasks, and improving accuracy through structured collaboration.
In practice, however, enterprise AI systems rarely remain confined to a single domain.
As adoption expands, ML platform teams in large enterprises encounter a different challenge. The question is not how to orchestrate agents...
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