In this episode, host Jonathan “J.” Tower sat down with Spencer Schneidenbach to talk about the practical realities of integrating AI into modern software applications. Rather than focusing on AI hype or novelty chatbots, J. and Spencer dig into what actually makes AI features valuable in production systems and why good AI engineering is still mostly good software engineering.  

They cover how to evaluate whether AI is the right solution for a business problem, the difference between AI-assisted development and AI-powered product features, and why many organizations are still misunderstanding what LLMs are actually good at. Spencer also shares lessons learned from production AI implementations, including how his team built systems that evaluate customer service calls using chained LLM workflows.  

If you’re trying to separate AI signal from noise, this episode is for you.

Guest Bio:Spencer Schneidenbach is an AI Architect, and the President and CTO of Aviron Labs. He has been recognized as a Microsoft MVP for his AI expertise and contributions to the community. You can find him sharing that expertise at events around the world and as a co-host of the new podcast, Agent Driven Development.

Additional Resources:

• Semantic Kernel: https://learn.microsoft.com/semantic-kernel/overview/

• Microsoft Agent Framework: search “Microsoft Agent Framework” on learn.microsoft.com

• Model Context Protocol (MCP): https://modelcontextprotocol.io

• Ollama (local models): https://ollama.com

• Hugging Face: https://huggingface.co

• Anthropic Engineering Blog: https://www.anthropic.com/engineering

• OpenAI Platform Documentation: https://platform.openai.com/docs

• Aviron Labs: https://www.avironlabs.com/