Orchestration framework for LLM-powered application logic
LangChain is a framework for building applications powered by language models chaining prompts, tool calls, retrieval steps, and memory together into a coherent workflow instead of hand-rolling that orchestration logic from scratch. It's particularly suited to agentic patterns, where a model needs to plan a sequence of steps and call external tools along the way.
Orchestration is where most of the real engineering risk in an AI feature actually lives not the model call itself, but what happens between calls: what tools the model can invoke, what it's allowed to do without approval, and how a bad plan gets stopped before it causes damage.
We use LangChain-style orchestration to build AI agents with bounded autonomy scoped, permissioned tool access, human approval gates on irreversible actions, and full action tracing, exactly as described in our AI agents case study. The framework handles the plumbing; the safety boundaries are engineering decisions we make on top of it.