Agent orchestration is moving from prompt choreography into code.
There’s a really cool feature of LangChain’s new dynamic subagents under that subagent headline. A root model can now write a tiny program, run it in an interpreter and have that program work out step by step within a narrow task() bridge. For example, a prompt can ask a model to cover every file, then the model can write a loop that enumerates those files instead of pretending the request itself creates coverage.
The piece we kept missing from the ‘ai agent orchestration’ conversation is: tell the model to plan, delegate, verify, retry, & synthesize. Then ask it to remember the entire flow as tool results get streamed back into the context window. Hope is not a strategy, and is expensive.
Documentation for dynamic subagents in LangChain dynamic subagents documentation explains the “cleaner contract”. In this model, the interpreter code dispatches subagents that were configured for the task at hand, in series, in branches, or in parallel in batches. The model is still deciding what work to do and the code is for coverage.
The long-standing harness problem in real AI agents real AI agents is showing up again here. The framework conversation has value ONLY when it graduates to deployment / tracing / evals / ownership. That runtime shift previewed at LangChain’s Interrupt runtime shift LangChain previewed at Interrupt conference is already manifesting in the orchestration layer.
