Enabling Agents In The Enterprise With A Platform Approach

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Jun 29, 2025

Arun Joseph, an AI engineering leader and entrepreneur, discusses his journey in developing multi-agent systems. He emphasizes the transformative potential of agentic capabilities in businesses and shares insights on building robust data models and orchestration loops. Arun tackles the challenges of managing large-scale data contexts, the importance of unified context management to avoid silos, and the shift toward open-source platforms like LMOS. He also explores how these innovations can enhance decision-making and streamline enterprise data management.

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ANECDOTE

Arun Joseph's Background

Arun Joseph has extensive experience building large-scale distributed and AI systems in enterprises like Deutsche Telekom.
He recently transitioned to entrepreneurship focusing on multi-agent systems for agentic capabilities.

INSIGHT

Agentic Orchestration Feedback Loop

Agentic orchestration works as a feedback loop enabling systems to iteratively refine actions toward a goal.
This paradigm allows business users to specify objectives which the system autonomously executes and improves.

ADVICE

Start Simple with Existing APIs

Start integrating language models with existing APIs simply before adopting new protocols like MCP or A2A.
Build incrementally and add complex layers as scalability demands increase, following the KISS principle.

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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

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.
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Links

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