Single agents can be useful—but many real-world applications rely on multiple agents working together. So how does that actually work?

In this episode of The Shift Podcast: Agentic Edition, members of the Microsoft Foundry team explore how multi‑agent systems are designed, coordinated, and evaluated. The discussion breaks down what “working together” means in practice—without assuming agents behave deterministically or perfectly.

Topics discussed include:

· What agent orchestration looks like in modern AI systems.

· How tool calling and function calling enable agents to collaborate.

· Why evaluation and iteration matter in multi‑agent workflows.

· The role of platforms and frameworks in coordinating agent behavior.

Rather than presenting a finished blueprint, the conversation reflects where the technology is today—highlighting tradeoffs, experimentation, and the realities of building with agents that interact dynamically.

This episode is grounded in how teams are actually building and learning from agentic systems, not how they might look in theory.

👉 Read the AI apps and agents e-book: https://aka.ms/AIAppsandAgents

👉 Join the Tech Community: https://techcommunity.microsoft.com/

Get to know the team:

· Takuto Higuchi, Product Strategy https://www.linkedin.com/in/takuto-higuchi-626681135

· Shawn Henry, Principal Group Product Manager https://www.linkedin.com/in/shawn-patrick-henry/

· Amanda Foster, Product Manager, Microsoft Foundry Agent Service https://www.linkedin.com/in/foster-amanda/

· Victor Dibia, Principal RDSE at Microsoft Core AI https://www.linkedin.com/in/dibiavictor/

The Shift Podcast: Agentic Edition is a place for experts to share their insights and opinions. As students of the future of technology, Microsoft values inputs from a diverse set of voices. That said, the opinions and findings of our guests are their own and they may not necessarily reflect Microsoft's own research or positions.

This episode of The Shift was recorded in January 2026. All information about products and offers is relevant to the time of recording.

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