Learn Agentic AI is an educational resource that teaches agentic AI development using the Dapr Agentic Cloud Ascent design pattern and agent-native cloud technologies.
The resource addresses the challenge of building production-grade agentic AI systems by providing structured learning around the DACA design pattern. It covers how to integrate OpenAI Agents SDK with Dapr for service orchestration, implement agent memory and model context protocol support, and deploy agents across Kubernetes environments. The curriculum spans foundational concepts through practical implementation using technologies like Dapr workflows, Dapr pub-sub messaging, knowledge graphs, and containerization with Docker and Rancher Desktop.
This resource suits developers and teams building agentic systems who want to understand cloud-native patterns for agent deployment and communication. It is particularly relevant for those planning to use Dapr as an orchestration layer, work with OpenAI's agent frameworks, or deploy agents in Kubernetes clusters. The material covers both the conceptual design pattern and the specific technology stack needed to implement it, making it useful for architects designing agent systems as well as engineers implementing them.
The project maintains active development with regular updates to course materials and examples. The repository demonstrates consistent engagement with the agentic AI space through ongoing refinement of the DACA pattern documentation and integration examples. The breadth of technologies covered—spanning message brokers like Kafka and RabbitMQ, data stores like PostgreSQL and Redis, and orchestration platforms—reflects an evolving understanding of production deployment scenarios for agent systems.
