Enabling Agents In The Enterprise With A Platform Approach

Data Engineering Podcast

Summary
In this episode of the Data Engineering Podcast Arun Joseph talks about developing and implementing agent platforms to empower businesses with agentic capabilities. From leading AI engineering at Deutsche Telekom to his current entrepreneurial venture focused on multi-agent systems, Arun shares insights on building agentic systems at an organizational scale, highlighting the importance of robust models, data connectivity, and orchestration loops. Listen in as he discusses the challenges of managing data context and cost in large-scale agent systems, the need for a unified context management platform to prevent data silos, and the potential for open-source projects like LMOS to provide a foundational substrate for agentic use cases that can transform enterprise architectures by enabling more efficient data management and decision-making processes.
Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I'm interviewing Arun Joseph about building an agent platform to empower the business to adopt agentic capabilities

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by giving an overview of how Deutsche Telekom has been approaching applications of generative AI?
  • What are the key challenges that have slowed adoption/implementation?
• Enabling non-engineering teams to define and manage AI agents in production is a challenging goal. From a data engineering perspective, what does the abstraction layer for these teams look like?
  • How do you manage the underlying data pipelines, versioning of agents, and monitoring of these user-defined agents?
• What was your process for developing the architecture and interfaces for what ultimately became the LMOS?
  • How do the principles of operatings systems help with managing the abstractions and composability of the framework?
• Can you describe the overall architecture of the LMOS?
  • What does a typical workflow look like for someone who wants to build a new agent use case?
  • How do you handle data discovery and embedding generation to avoid unnecessary duplication of processing?
• With your focus on openness and local control, how do you see your work complementing projects like Oumi
• What are the most interesting, innovative, or unexpected ways that you have seen LMOS used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on LMOS?
• When is LMOS the wrong choice?
• What do you have planned for the future of LMOS and MASAIC?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

• LMOS
• Deutsche Telekom
• MASAIC
• OpenAI Agents SDK
• RAG == Retrieval Augmented Generation
• LangChain
• Marvin Minsky
• Vector Database
• MCP == Model Context Protocol
• A2A (Agent to Agent) Protocol
• Qdrant
• LlamaIndex
• DVC == Data Version Control
• Kubernetes
• Kotlin
• Istio
• Xerox PARC)
• OODA (Observe, Orient, Decide, Act) Loop

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA