Are your AI agent projects failing to move past the proof of concept stage into production? In this episode of the M365 FM Podcast, Microsoft AI MVP and CTO Kartikeyan joins us to reveal why too much reliance on LLMs and a lack of core engineering is the silent killer of enterprise AI. We dive deep into the architectural shift needed to move from probabilistic experiments to deterministic, reliable business solutions.

Throughout the conversation, Kati shares his twenty one years of experience to explain why treating AI agents like sophisticated batch jobs is the key to success. You will learn about the critical roles of orchestration, state management, and evaluation loops in building systems that actually work for businesses. We also explore the practical benefits of Azure AI Foundry and why the future of the enterprise might belong to Small Language Models rather than massive general purpose ones.

Key topics covered in this episode include:

The difference between a real AI agent and a simple prompt.
Why most AI projects fail due to poor memory design and lack of boundaries.
How to implement observability to trace the reasoning parts of an LLM.
The transition from proof of concept to a scalable three layer architecture.
Practical tips for governance and preventing agents from deleting your databases.

Chapters

0:00 Introduction and Katis Journey to Microsoft MVP
3:10 Getting Started with Azure AI and Machine Learning
6:45 Why Azure AI Foundry is a Game Changer for Developers
10:15 The Evolution from Machine Learning Studio to AI Agents
14:30 The Core Problem: Too Much LLM and Not Enough Engineering
18:00 Defining a Real AI Agent vs a Standard Batch Job
22:15 Missing Engineering Components in AI Architecture
26:00 Deep Dive into Orchestration, Memory, and Evaluation
30:30 The Reality and Future of Multi Agent Systems
34:45 Common Architectural Mistakes in Enterprise AI
39:00 Solving for Scalability and Reliability in Production
42:15 Observability, Monitoring, and Tracing AI Decisions
45:00 Governance, Responsible AI, and Data Privacy
47:20 Small Language Models vs General Purpose LLMs
48:45 Rapid Fire Round and Final Advice for Organizations

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