LangChain publishes the line between a harness, a framework and a runtime

LangChain draws its own product boundaries along lines the corpus argues about. LangGraph is called the agent runtime, LangChain the agent framework, Deep Agents the agent harness, and the harness is defined by function: "The job of an agent harness is to get the right context to the model at the right time via context engineering." What it bundles is the list practitioners keep rebuilding: a filesystem for context that should not sit in the context window, subagents, on-demand skills, cross-run memory. The structural admission is a one-liner: "Deep Agents is actually just the core LangChain agent plus a bunch of middleware." One first-party production figure, on their own GTM agent built on deepagents: "almost 10k requests per week, and over 150 active users. 26% of the traffic is user initiated, and the remaining 74% is driven by ambient agent work."

Why it matters: The 74% ambient figure measures where an agent's work actually originates in production, which is not what a demo predicts. The layer vocabulary travels even if you never touch LangChain: most arguments about agent frameworks are two people describing different layers.