From Demo to Production- Building Enterprise AI Agents That Actually Work with Microsoft Copilot Studio with Elliot Margot [MVP]

In this episode of the M365 Show, Mirko Peters speaks with Elliot Margot, Microsoft MVP for Microsoft 365 Copilot and Copilot Studio Team Lead at Witivio. Elliot shares a practical perspective on taking AI from an impressive demo to a secure, governed and genuinely useful enterprise solution. The conversation covers Microsoft Copilot Studio, multi-agent systems, enterprise RAG, MCP, Power Automate, governance, cost control and the human side of AI adoption.ㅤㅤFROM CHATBOTS TO ENTERPRISE AI AGENTSThe gap between a chatbot demo and a production-ready agent is much larger than it first appears. An enterprise agent needs a clear purpose, reliable data, carefully scoped tools, sensible fallback paths and a way for people to understand what it is doing. Elliot explains that organisations should not wait for a perfect governance model before experimenting—but they also cannot deploy AI blindly. The strongest approach is to learn by building small, useful solutions while steadily improving guardrails, monitoring and operating models.ㅤㅤGOVERNANCE IS A JOURNEY, NOT A BLOCKERGovernance, security and compliance are often the reasons enterprises hesitate to start with AI. Elliot makes the case for a balanced approach: establish the foundations, understand where data goes, apply Data Loss Prevention policies and sensitivity labels, but keep moving. Companies gain the most useful governance insights from real usage. AI evolves quickly, so governance cannot be treated as a one-time project; it requires ownership, continuous learning and administrators who know where to find the right controls across Microsoft 365, Power Platform, Purview and Copilot administration.ㅤㅤSELLING AI THROUGH REAL BUSINESS VALUEㅤExecutive sponsorship is not only about promising headcount reduction. The better conversation is about improving service, reducing repetitive work and giving teams more time for work that requires judgement and human connection. Elliot uses the example of IT support: even a modest reduction in repetitive Level 1 tickets can create meaningful value. The most convincing AI projects combine a clear business case with a strong “wow” moment that helps people understand what is now possible.ㅤㅤWHY MULTI-AGENT SYSTEMS MATTERA single general-purpose agent can attempt many tasks, but specialised agents can deliver more reliable results. Elliot describes a multi-agent approach where different agents take on distinct roles, such as creating content, reviewing quality, checking user experience, validating requirements or orchestrating a workflow. Instead of expecting one model to get everything right on the first attempt, a multi-agent system can improve, audit and refine its work. This is how AI starts to resemble a coordinated digital team rather than a simple prompt-and-response experience.ㅤㅤRAG, METADATA AND BETTER KNOWLEDGE RETRIEVALEnterprise AI is only as useful as the information it can retrieve. Elliot explains why metadata is essential for effective RAG implementations. Documents should have clear descriptions, languages, classifications and relevant tags so an agent can retrieve the right source quickly and avoid unnecessary token consumption. A large collection of poorly structured PDFs, duplicate files and outdated versions creates slow, expensive and unreliable answers. Good knowledge architecture means that the current approved information is available to the agent, while old versions are kept out of the production knowledge source.ㅤㅤMCP AND CONNECTING AGENTS TO THE ENTERPRISEModel Context Protocol, or MCP, is becoming an important way to connect AI agents with enterprise tools and APIs. Elliot explains MCP as a structured, discoverable bundle of capabilities that tells an agent what tools are available and how to use them. Instead of treating every API as an isolated endpoint, MCP can help package connections in a more consistent, secure and reusable way. For enterprise AI, this matters because agents need to work with real business systems—not just generate text in a chat window.ㅤㅤCOPILOT STUDIO, POWER PLATFORM AND PRODUCTION READINESSㅤCopilot Studio opens AI development to more people, but building faster does not remove the need for responsibility. Elliot discusses the growing role of citizen development, prompt-driven building and AI-assisted creation across Power Platform. He also stresses that every production solution must be tested properly. Automated test prompts are valuable, but manual testing remains essential. Do not assume that an agent is ready for production simply because another AI says the workflow looks correct. Human review, scenario testing and clear ownership remain vital.ㅤㅤDLP, PURVIEW AND KEEPING AGENT SCOPE SMALLA strong security model starts with scope. If an agent is meant to summarise Teams meetings, Outlook messages and daily tasks, it should only have access to the relevant services. Elliot recommends separating use cases into appropriate Power Platform environments and applying targeted DLP policies, rather than creating one broad environment with unrestricted access. Microsoft Purview adds another important layer through sensitivity labels and information protection, helping organisations avoid exposing confidential, HR or regulated content to agents that do not need it.ㅤㅤREAL-WORLD USE CASE: THE RFP AGENTOne of the most practical examples in this episode is an RFP agent that helps automate procurement processes. The agent supports users from the initial request through preparing documentation, handling supplier questions, analysing proposals and communicating outcomes. Human decision-makers stay involved at the important points, but the repetitive administrative work is dramatically reduced. This kind of solution shows where enterprise AI becomes valuable: it does not replace accountability, but it removes friction from complex processes.ㅤㅤSMALL MODELS, TOKEN CONTROL AND FINOPSNot every task needs the most powerful and expensive model. Elliot explains why organisations need to match model capability to the actual job. A lightweight model can be ideal for summarisation, classification and predictable workflows, while more capable models should be reserved for more complex reasoning. Cost management is not optional in agentic systems. Agents need limits, monitoring and safe escalation paths so they do not get stuck in endless loops, repeatedly calling tools and producing unexpected bills. Good FinOps means understanding consumption, agent usage, model selection and the value delivered by each workload.ㅤㅤTHE FUTURE: AGENTS, SKILLS AND AI LITERACYElliot’s view is that not every business will run huge multi-agent systems, but more employees will use AI agents as part of their normal work. The key skills will be clear communication, AI literacy and the ability to recognise a real business pain worth solving. People do not need to understand every detail of model training, but they do need to understand how to describe a task, choose an appropriate tool, validate the result and work safely with data. The best starting point is often a small flow in Power Automate: test it, monitor it, learn from it and build from there.Listen to the full episode for practical insights on Microsoft Copilot Studio, enterprise AI agents, agent governance, RAG, metadata, MCP integrations, DLP, Power Platform and building AI solutions that work beyond the demo.Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365--6704921/support.