Everybody seems to be building agentic AI systems these days. These systems are essentially distributed applications, involving communication across LLM providers, services, and data stores.

In this session I'll show how Dapr, the Distributed Application Runtime, a graduated CNCF project, helps to build and run these agentic systems reliably using the durable execution principle that is provided by Dapr Workflow. I'll demonstrate various patterns in .NET for building effective agents such as Prompt Chaining, Routing, Parallelization, Orchestrator-Workers and Evaluator-Optimizer. I'll also use the new LLM Conversation API in Dapr to interact with different LLM providers.

By the end of the session, you'll have a good understanding of how to build agentic systems with Dapr, and when to apply the different agentic patterns.