Regisseur and LangChain. The agent and the scaffold.

LangChain is infrastructure. Regisseur is the production scaffold.

LangChain is the most important piece of open-source AI infrastructure shipped in the last three years. If your team is building a custom agent, LangChain and its ecosystem (LangGraph, LangSmith) are where you should start. The framework gives a developer a clean way to compose model calls, tool use, memory, and graph-shaped control flow into a working agent. We use LangChain primitives in our own product. Anyone who tells you otherwise is selling you something.

But LangChain is a framework. Regisseur is a platform. These are different things, and the distinction matters most when a regulated enterprise tries to put an AI agent into production.

When engineering should own the whole production layer.

You are a developer-led team. You want to build one custom agent that solves one specific problem. You will own the runtime, the observability, the deployment, the integration with your systems of record, the policy enforcement, the human-in-the-loop UX, the audit trail, the emergency stop, the versioning, the rollback, and every operational concern that turns a working agent in a notebook into a working agent in production. You have the engineering capacity to build all of that.

If that describes you, pick LangChain. We mean this seriously. It is the right answer for that situation and we will not try to talk you out of it.

When regulated operations need configurable scaffolding.

You are an operations leader at a regulated enterprise. You need AI agents to handle real work — underwriting cases, claims, access requests, onboarding, incidents — under conditions that include a regulator who can ask you in three years to reconstruct exactly why a decision was made, a Chief Risk Officer who will not approve any system without an emergency brake, a Chief Underwriting Officer who needs humans in the loop on specific decisions and only those, and a CIO who is unwilling to add another developer-owned system to the production stack.

You do not want to build the scaffolding. You want to configure it. You want the audit trail, the autonomy ceiling, the human review gates, the versioning, the emergency brake, the on-premises deployment option, the provider abstractions, and the multi-system orchestration to be primitives, not engineering work.

That is what Regisseur is. The agent is one piece of it. The scaffolding around the agent is the rest.

LangChain composes agents. Regisseur governs operational work.

LangChain composes model calls into agent graphs. That's the level of the framework. To run that agent in production, your team builds — or buys — every layer above:

• A workflow engine to govern what the agent is allowed to do, in what order, against which inputs.
• A case model that ties the agent's work to a unit of regulated work.
• An autonomy ceiling that the agent cannot exceed regardless of what the model decides.
• A human review queue that the agent surfaces decisions into, with the right reviewer routed for the right decision.
• An audit trail that can reconstruct every decision against the rules and tools and prompt versions that were live at the moment of the decision.
• A provider abstraction so the agent does not crash when DocuSeal moves a webhook or your email provider rotates a token.
• An emergency brake that halts every agent in flight across every workspace in one click.

Regisseur ships all of that as the engine. The agent slots into it.