Agentic Mesh with Eric Broda

Agentic Mesh with Eric Broda

Author: Software Engineering Daily April 16, 2026 Duration: 47:23

AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability.

Eric Broda is a veteran of the software industry, and he’s the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem.

In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

Please click here to see the transcript of this episode.

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Dive deep into the conversations shaping the future of artificial intelligence with the Machine Learning Archives-Software Engineering Daily. This curated collection pulls from the broader Software Engineering Daily library, focusing entirely on the intricate world of ML engineering. Each episode features a detailed, technical interview with engineers, researchers, and architects who are building the systems behind today's most advanced AI. You'll hear them break down complex topics like model deployment, data pipeline challenges, and the practical trade-offs involved in taking research from a notebook into production. The discussions are grounded in real-world implementation, moving beyond theoretical concepts to explore the tools, failures, and successes that define the field. For developers and technical leaders looking to understand the nuts and bolts of applied machine learning, this podcast offers a valuable archive of knowledge. It's a direct line to the practitioners who are solving hard problems every day, providing insights you can apply to your own work. Tune in to gain a clearer perspective on how machine learning is integrated into modern software, one detailed conversation at a time.
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