WB-40 (330) Agentic
Jun 14, 2025
Chris Weston, a software development expert at NashTech involved in InsureTech and education events, joins the hosts for an engaging discussion on Agentic AI. They explore how this innovative AI operates autonomously, learning and adapting rather than just reacting. Chris highlights the risks, including accountability issues and the potential decline in AI quality due to demand. Legal challenges in AI hiring practices, particularly concerning age discrimination, are also examined, shedding light on the urgent need for oversight in this evolving field.
AI Snips
Chapters
Transcript
Episode notes
What Is Agentic AI?
- Agentic AI actively pursues outcomes autonomously and learns from mistakes, unlike basic agents that follow fixed rules or commands.
- It can collaborate with other AI agents and adapt methods to achieve goals proactively.
Agentic AI in Recruitment
- The example of agentic AI recruiting project managers illustrates how it can handle tedious tasks by continuously monitoring the market and updating candidate lists.
- It proactively improves its methods, saving time and effort in recruitment processes.
Autonomy Defines Agentic AI
- The key distinction for agentic AI is setting it an outcome to achieve autonomously, going beyond simple machine learning tasks.
- It uses external knowledge, iterates, and optimizes methods to deliver results, resembling rolling loaded dice towards favorable outcomes.