Summary
In 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

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Your host is Tobias Macey and today I'm interviewing Guillaume de Saint Marc about the complexities and opportunities of scaling multi-agent systems

Interview

Introduction
How did you get involved in machine learning?
Can you start by giving an overview of what constitutes a "multi-agent" system?
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?
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?
- What is the role of humans in that equation?
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?
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?
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?
What are the most interesting, innovative, or unexpected ways that you have seen AGNTCY/multi-agent systems used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on multi-agent systems?
When is a multi-agent system the wrong choice?
What do you have planned for the future of AGNTCY/multi-agent systems?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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