Large companies are building shared control layers before scaling AI agents

Companies moving AI agents from pilots into production are increasingly building a common operating layer rather than letting every team deploy its own stack. LangChain’s review of projects at Schneider Electric, Vodafone, and monday.com shows the same problem across different industries: agents are quick to prototype but difficult to monitor, evaluate, and control at scale.

Schneider Electric runs an internal AI Hub with 350 people supporting more than 60 agents across critical-infrastructure work. Its approach treats observability, evaluation, and deployment as a shared discipline instead of rebuilding those capabilities for every project. Vodafone uses LangGraph for two production assistants, Insight Engine and Enigma, and LangSmith to inspect and improve their behavior.

monday.com changed the architecture of its Sidekick assistant after learning that simply adding more tools made the general-purpose agent perform worse. The company moved to a layered design with specialized subagents, bounded tools, and sandboxes. That limits what each component can do and makes failures easier to isolate.

These are individual company examples, not proof that one architecture fits every organization. They do point to a practical threshold: once multiple business units have agent pilots, the bottleneck shifts from model capability to operational consistency. Shared evaluation, permissions, and tracing become prerequisites for expansion, especially in regulated or business-critical environments.