Summary
In this episode of the AI Engineering Podcast, host Tobias Macey sits down with Ben Wilde, Head of Innovation at Georgian, to explore the transformative impact of agentic AI on business operations and the SaaS industry. From his early days working with vintage AI systems to his current focus on product strategy and innovation in AI, Ben shares his expertise on what he calls the "continuum" of agentic AI - from simple function calls to complex autonomous systems. Join them as they discuss the challenges and opportunities of integrating agentic AI into business systems, including organizational alignment, technical competence, and the need for standardization. They also dive into emerging protocols and the evolving landscape of AI-driven products and services, including usage-based pricing models and advancements in AI infrastructure and reliability.

Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Ben Wilde about the impact of agentic AI on business operations and SaaS as we know it

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by sharing your definition of what constitutes "agentic AI"?
• There have been several generations of automation for business and product use cases. In your estimation, what are the substantive differences between agentic AI and e.g. RPA (Robotic Process Automation)?
  • How do the inherent risks and operational overhead impact the calculus of whether and where to apply agentic capabilities?
• For teams that are aiming for agentic capabilities, what are the stepping stones along that path?
• Beyond the technical capacity, there are numerous elements of organizational alignment that are required to make full use of the capabilities of agentic processes. What are some of the strategic investments that are necessary to get the whole business pointed in the same direction for adopting and benefitting from AI agents?
• The most recent splash in the space of agentic AI is the introduction of the Model Context Protocol, and various responses to it. What do you see as the near and medium term impact of this effort on the ecosystem of AI agents and their architecture?
• Software products have gone through several major evolutions since the days of CD-ROMs in the 90s. The current era has largely been oriented around the model of subscription-based software delivered via browser or mobile-based UIs over the internet. How does the pending age of AI agents upend that model?
• What are the most interesting, innovative, or unexpected ways that you have seen agentic AI used for business and product capabilities?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working with businesses adopting agentic AI capabilities?
• When is agentic AI the wrong choice?
• What are the ongoing developments in agentic capabilities that you are monitoring?

Contact Info

• Email
• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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Links

• Georgian
• Agentic Platforms And Applications
• Differential Privacy
• Agentic AI
• Language Model
• Reasoning Model
• Robotic Process Automation
• OFAC
• OpenAI Deep Research
• Model Context Protocol
• Georgian AI Adoption Survey
• Google Agent to Agent Protocol
• GraphQL
• TPU == Tensor Processing Unit
• Chris Lattner
• CUDA
• NeuroSymbolic AI
• Prolog

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0