Making Data Simple
Making Data Simple

Replay: Agentic AI and the End of Deterministic Decision Trees with Megan Gallagher from Maven AGI

26 November 2025 55:01 Making Data Simple

Listen to episode

About this episode

Send us a text

Hit replay on one of the most thought-provoking Agentic AI conversations on Making Data Simple. GTM Account Director Megan Gallagher makes the case for Agentic AI from the Maven AGI front lines, where AI agents stop following rigid decision trees and start acting with real autonomy over enterprise workflows.

“We’re still living like everything is deterministic,” Megan argues, “but this new generation of agents is inherently generative and predictive.” In this replay, she unpacks what that shift means for smaller specialized models, using real enterprise data, rethinking “assistant vs person,” and how to get started without boiling the ocean.

If you want to understand how Agentic AI moves from slideware to shipped value, this is the episode to queue up again.


  • 01:30 All Great Podcasts start with Drinks
  • 05:27 Maven AGI 09:13 Smaller Models! 10:50 Why Maven AGI
  • 12:04 The Secret Sauce or Use Case
  • 15:13 Typical Client Persona 20:31 Using Enterprise Data 26:19 But AGI, Really?
  • 30:12 Assistant or Person?
  • 39:06 What's Next?
  • 40:28 My Thoughts on Getting Started?
  • 46:30 The AI Example
  • 49:30 The Maven AGI Pitch
  • 53:23 Learning


Maven AGI: https://www.mavenagi.com/ 

Megan's LinkedIn: https://www.linkedin.com/in/megfgallagher/

Al's LinkedIn: https://www.linkedin.com/in/al-martin-ku/

#AgenticAI #FutureOfAI #MakingDataSimple #MavenAGI #AIAgents #EnterpriseAI #CustomerExperience #AIInProduction #PodcastReplay​

Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Making Data Simple. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.