SummaryIn this episode Guillaume de Saint Marc, VP of Engineering at Cisco Outshift, talks about the complexities and opportunities of scaling multi‑agent systems. Guillaume explains why specialized agents collaborating as a team inspire trust in enterprise settings, and contrasts rigid, “lift-and-shift” agentic workflows with fully self-forming systems. We explore the emerging Internet of Agents, the need for open, interoperable protocols (A2A for peer collaboration and MCP for tool calling), and new layers in the stack for syntactic and semantic communication. Guillaume details foundational needs around discovery, identity, observability, and fine-grained, task/tool/transaction-based access control (TBAC), along with Cisco’s open-source Agency initiative, directory concepts, and OpenTelemetry extensions for agent traces. He shares concrete wins in IT/NetOps—network config validation, root-cause analysis, and the CAPE platform engineer agent—showing dramatic productivity gains. We close with human-in-the-loop UX patterns for multi-agent teams and SLIM, a high-performance group communication layer designed for agent collaboration.Announcements<ul><li>Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems</li><li>When ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at <a href="https://www.aiengineeringpodcast.com/prefect" target="_blank">aiengineeringpodcast.com/prefect</a> today.</li><li>Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at <a href="https://www.aiengineeringpodcast.com/bruin" target="_blank">aiengineeringpodcast.com/bruin</a>, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.</li><li>Your host is Tobias Macey and today I'm interviewing Guillaume de Saint Marc about the complexities and opportunities of scaling multi-agent systems</li></ul>Interview<ul><li>Introduction</li><li>How did you get involved in machine learning?</li><li>Can you start by giving an overview of what constitutes a "multi-agent" system?</li><li>Many of the multi-agent services that I have read or spoken about are designed and operated by a single department or organization. What are some of the new challenges that arise when allowing agents to communicate and co-ordinate outside of organizational boundaries?</li><li>The web is the most famous example of a successful decentralized system, with HTTP being the most ubiquitous protocol powering it. What does the internet of agents look like?<ul><li>What is the role of humans in that equation?</li></ul></li><li>The web has evolved in a combination of organic and planned growth and is vastly more complex and complicated than when it was first introduced. What are some of the most important lessons that we should carry forward into the connectivity of AI agents?</li><li>Security is a critical aspect of the modern web. What are the controls, assertions, and constraints that we need to implement to enable agents to operate with a degree of trust while also being appropriately constrained?</li><li>The AGNTCY project is a substantial investment in an open architecture for the internet of agents. What does it provide in terms of building blocks for teams and businesses who are investing in agentic services?</li><li>What are the most interesting, innovative, or unexpected ways that you have seen AGNTCY/multi-agent systems used?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on multi-agent systems?</li><li>When is a multi-agent system the wrong choice?</li><li>What do you have planned for the future of AGNTCY/multi-agent systems?</li></ul>Contact Info<ul><li><a href="https://www.linkedin.com/in/guillaume-de-saint-marc-54928a1/" target="_blank">LinkedIn</a></li></ul>Parting Question<ul><li>From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?</li></ul>Closing Announcements<ul><li>Thank you for listening! Don't forget to check out our other shows. The <a href="https://www.dataengineeringpodcast.com" target="_blank">Data Engineering Podcast</a> covers the latest on modern data management. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used.</li><li>Visit the <a href="https://www.aiengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email hosts@aiengineeringpodcast.com with your story.</li><li>To help other people find the show please leave a review on <a href="https://podcasts.apple.com/us/podcast/the-machine-learning-podcast/id1626358243" target="_blank">iTunes</a> and tell your friends and co-workers.</li></ul>Links<ul><li><a href="https://outshift.cisco.com/" target="_blank">Outshift by Cisco</a></li><li><a href="https://en.wikipedia.org/wiki/Multi-agent_system" target="_blank">Multi-Agent Systems</a></li><li><a href="https://en.wikipedia.org/wiki/Deep_learning" target="_blank">Deep Learning</a></li><li><a href="https://meraki.cisco.com/" target="_blank">Meraki</a></li><li><a href="https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence" target="_blank">Symbolic Reasoning</a></li><li><a href="https://en.wikipedia.org/wiki/Transformer_(deep_learning)" target="_blank">Transformer Architecture</a></li><li><a href="https://www.deepseek.com/" target="_blank">DeepSeek</a></li><li><a href="https://en.wikipedia.org/wiki/Reasoning_model" target="_blank">LLM Reasoning</a></li><li><a href="https://en.wikipedia.org/wiki/Ren%C3%A9_Descartes" target="_blank">René Descartes</a></li><li><a href="https://en.wikipedia.org/wiki/Kanban" target="_blank">Kanban</a></li><li><a href="https://a2a-protocol.org/latest/" target="_blank">A2A (Agent-to-Agent) Protocol</a></li><li><a href="https://modelcontextprotocol.io/docs/getting-started/intro" target="_blank">MCP == Model Context Protocol</a></li><li><a href="https://agntcy.org/" target="_blank">AGNTCY</a></li><li><a href="https://www.icann.org/" target="_blank">ICANN == Internet Corporation for Assigned Names and Numbers</a></li><li><a href="https://en.wikipedia.org/wiki/OSI_model" target="_blank">OSI Layers</a></li><li><a href="https://opencontainers.org/" target="_blank">OCI == Open Container Initiative</a></li><li><a href="https://docs.agntcy.org/oasf/open-agentic-schema-framework/?type=White%20Papers?type=eBooks" target="_blank">OASF == Open Agentic Schema Framework</a></li><li><a href="https://github.com/oracle/agent-spec" target="_blank">Oracle AgentSpec</a></li><li><a href="https://www.splunk.com/" target="_blank">Splunk</a></li><li><a href="https://opentelemetry.io/" target="_blank">OpenTelemetry</a></li><li><a href="https://github.com/cnoe-io/ai-platform-engineering?tab=readme-ov-file" target="_blank">CAIPE == Community AI Platform Engineer</a></li><li><a href="https://docs.agntcy.org/coffee-agntcy/get-started/" target="_blank">AGNTCY Coffee Shop</a></li></ul>The intro and outro music is from <a href="https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/" target="_blank">Hitman's Lovesong feat. Paola Graziano</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a>/<a href="https://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA 3.0</a>

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