Conceptual Guide Scaling Agents in Europe & The Middle East: Lessons f

Conceptual Guide Scaling Agents in Europe & The Middle East: Lessons from Schneider Electric, Vodafone, and monday.com Jess Ou September 3, 2026 17 min Go back to blog Create agents Share Agent programs in the region are taking a different path from the consumer-facing applications that get a lot of attention. Fewer teams are starting with a single flashy chatbot. More are starting with a platform, often because they already have a dozen agent proofs of concept scattered across business units, and no consistent way to bring them into production. This is a pattern we’re seeing appear across industries with very different levels of regulatory pressure. From energy and telecom to insurance, banking, and retail, the underlying challenge is consistent. Companies are finding agents easy to prototype and much harder to operate. Operating well requires an infrastructure layer that many teams did not anticipate when they built their first agent. This piece looks at how three companies built that infrastructure layer: Schneider Electric runs an internal AI Hub of 350 people supporting more than 60 agents across critical infrastructure, with an LLMOps discipline built around observability, evaluation, and deployment monday.com rebuilt its AI assistant, Sidekick, from a single general-purpose agent into a layered system of subagents, bounded tools, and sandboxes after discovering in production that adding more tools was making the agent worse, not better Vodafone built two production assistants, Insight Engine and Enigma, on LangGraph and uses LangSmith to monitor and improve them Alongside these three, we’ll draw on patterns emerging across a broader set of agent programs we’re seeing in the region, spanning industries from insurance and security operations to consumer retail. The emerging patterns in agent programs Central agent platforms are consolidating fragmented agents across the enterprise. The most common pattern in the region right now is that teams are investing in a platform, rather than building single agents. 35% of organizations we speak to describe a company-wide agent platform or control plane as the primary use case, with business-unit agents eventually running on top of it. Companies with a dozen or more agent efforts underway tend to reach the same conclusion: individual teams are rebuilding the same foundations, and someone needs to own the shared layer. That can mean vetted, reusable templates or frameworks that keep teams from reinventing the basics, or consolidating decentralized development into a central AI hub that supports the full lifecycle. At this stage, it is not unusual for a company to have hundreds of proofs of concept but no clear path to production for most of them. A central agent platform can solve this problem. Many organizations are finding ROI from building agents for regulated document and back-office work. 18% of organizations we speak to are focusing on claims, underwriting, invoices, tenders, procurement, payroll, or other workflows with both an existing paper trail and a known cost per case. Agents for policy-wording review, claims-document classification, loss-run extraction, and invoice validation are all in production. In many cases, work that once took hours can now be completed in minutes. Risk, compliance, and security operations use cases are rising in the region. 12% of organizations we speak to are building agents for reducing analyst workload in functions that carry audit obligations. Examples include triaging low- and medium-severity security alerts, running second-line assurance and anti-financial-crime testing, and automating flagged-transaction verification with an attached confidence score. Federated building begins once engineering starts to become a bottleneck. 16% of organizations are trying to help non-engineers build agents with central guardrails. The common pattern is enabling low-code and non-technical users to configure agents, while engineers industrialize the ones that work. To accomplish this, teams are building global platforms (leveraging products such as LangSmith Fleet , offered headless for enterprises) where teams can configure, evaluate, and publish agents without writing code, while central teams enforce the standards required for production. Observability, evals, and cost control are the foundation for scale. This is the most common theme across our conversations. Observability is increasingly tied to governance and spend, alongside debugging. Teams want tracing, evals, prompt management, and annotation queues across dozens of use cases at once. Increasingly, teams are also putting an LLM gateway at the center of the roadmap to create unified visibility across users, models, tokens, spend, and policy before agents are given broader autonomy. The three teams below show what it takes to operate agents once a company has moved beyond its first pilot. Three leading teams building agents in production Schneider Electric: LLMOps as a shared discipline across 60+ agents Schneider Electric is a global energy technology leader, driving sustainability by electrifying, automating, and digitalizing industries, businesses, and homes. With 160,000 employees and roughly 40 billion euros in annual revenue, the company runs an ambitious AI program: an internal AI Hub of 350 experts who have deployed 60+ agents to optimize energy consumption, extend asset lifecycles, and accelerate developer productivity. Schneider's broad AI program spans three categories: Embedding intelligence directly into products to cut energy consumption (such as thermal learning in room controllers) Using AI to forecast demand and production so customers can shift electricity usage toward cheaper, greener times of day Deploying agentic copilots that reduce operational friction , like managing a more complex grid, customer success, or querying a carbon emissions software system Agents are embedded across these objectives, operating in critical infrastructure with strict data residency requirements and cybersecurity controls. Schneider needed a common agent platform that could help teams build quickly while preserving control over data, deployment, and quality. “The challenge of accuracy, the challenge of quality of answers, the challenge of guardrailing, are very real. When you deploy a solution at scale, you need tooling like LangSmith. Everything linked with trustability and understanding what happens is extremely valuable for us.” — Philippe Rambach, CAIO at Schneider Electric Schneider’s AI Platform team sits within their AI Hub and provides the shared infrastructure that enables AI squads to reliably deliver across their vast technology landscape (multi-cloud, from cloud to the edge, and all types of AI). They’ve built LLMOps capabilities around LangSmith and the broader LangChain ecosystem to: Deploy and continuously improve the accuracy and quality of an AI Assistant serving 140,000 employees in 100+ countries Co-build an LLMOps maturity framework to deploy their Customer Success Manager Copilot Accelerate their quotation workflow with LangSmith Deployment' s task-queue model Observability Schneider self-hosts LangSmith on AWS EKS behind its own security perimeter. One of its most important structural decisions was to create one workspace per AI product spanning every environment, from development through production, rather than creating separate workspaces for each environment. Structuring this way facilitates the improvement loop. Production traces can flow back into development datasets for offline evaluation, while subject-matter experts can annotate a production trace and push it directly into a dataset. One Jo , Schneider’s internal AI assistant, serves 160,000 employees across 107 countries. Every conversation is traced, and production traces are systematically reused to build regression datasets and detect drift. Production example: "One Jo" annotation queue Evaluation Schneider has invested in evaluation on three fronts. First, it built offline evaluation templates , standardizing dataset conventions and evaluator interfaces across squads. Second, it created an LLMOps maturity framework that scores each of its 60+ products on instrumentation, offline evals, online evals, and feedback loops, then uses those scores to gate progression from exploration to industrialization. The LLM Ops Loop ‍ Third, Schneider has brought subject-matter experts directly into the evaluation process. About 20% of its AI products now have at least one active annotation queue where SMEs review real production examples. Its Customer Success Manager Copilot, used by more than 250 CSMs, was built with SMEs involved from the beginning, which the team credits with helping it reach high quality and adoption at launch. Production example: Customer Success Manager chatbot in LangSmith Experiments Deployment Rather than running every agent on one centralized runtime, Schneider standardizes on the Lang