Episode
Understanding The Operational And Organizational Challenges Of Agentic AI
- Podcast
- AI Engineering Podcast
- Published
- Apr 21, 2025
- Duration seconds
- 4336
- Processing state
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Summary
Summary In this episode of the AI Engineering podcast Julian LaNeve, CTO of Astronomer, talks about transitioning from simple LLM applications to more complex agentic AI systems. Julian shares insights into the challenges and considerations of this evolution, emphasizing the importance of starting with simpler applications to build operational knowledge and intuition. He discusses the parallels between microservices and agentic AI, highlighting the need for careful orchestration and observability to manage complexity and ensure reliability, and explores the technical requirements for deploying AI systems, including data infrastructure, orchestration tools like Apache Airflow, and understanding the probabilistic nature of AI models. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Seamless data integration into AI applications often falls short, leading many to adopt RAG methods, which come with high costs, complexity, and limited scalability. Cognee offers a better solution with its open-source semantic memory engine that automates data ingestion and storage, creating dynamic knowledge graphs from your data. Cognee enables AI agents to understand the meaning of your data, resulting in accurate responses at a lower cost. Take full control of your data in LLM apps without unnecessary overhead. Visit aiengineeringpodcast.com/cognee to learn more and elevate your AI apps and agents. Your host is Tobias Macey and today I'm interviewing Julian LaNeve about how to avoid putting the cart before the horse with AI applications. When do you move from "simple" LLM apps to agentic AI and what's the path to get there? Interview Introduction How did you get involved in machine learning?…