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Most AI agent frameworks treat context as an afterthought, leaving developers to reimplement LLM concerns inside their own domain logic. The post argues this creates fragile, complex systems and introduces Mozaik, a library built on the OpenResponses specification that provides a rich domain model for context management in agentic applications. Mozaik models context as a first-class primitive composed of typed context items (user messages, developer messages, model-generated items as state machines), enabling developers to keep LLM concerns separate from business logic. A minimal code example demonstrates building, storing, and restoring context using an in-memory repository with a GPT-4 model call.

Why Agent Frameworks End Up As SDK Wrappers — And How To Overcome It

We are building an operating system for AI agents. We want to enable every developer to create and, if possible, control autonomous AI workflows. Here we are sharing content on this topic.