Building the Foundation for the Agentic AI Era
Episode
45 min
Read time
2 min
Topics
Fundraising & VC, Marketing, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓Enterprise AI Adoption — The 1-9-90 Champion Model: To scale AI adoption across 3,500 engineers, identify the top 1% of enthusiasts and form a cohort of ~50 cross-functional champions representing every major repo type (frontend, backend, iOS, Android). Give them 30% of their time to experiment, then have them embed working techniques directly into repositories so all teammates benefit automatically without individual retraining.
- ✓Context Engineering Over Individual Training: Rather than teaching every developer prompt techniques, embed agent instructions, skills, and context files directly into the codebase. When engineers point their AI agent at a repo, the agent inherits the team's preferred behavior automatically. This approach separates the knowledge-building burden from the individual user, making adoption frictionless at scale across large monorepos.
- ✓Non-Technical AI Onboarding — Lead With Data Access, Not Automation: When introducing agents to finance, HR, or marketing teams, start with a concrete time-saving win: give them instant access to database records they previously had to request from another team. Bypassing multi-day report request cycles builds immediate trust. Once trust is established, employees naturally expand delegation to more core tasks at their own pace.
- ✓Neutral Standards Homes Accelerate Enterprise Adoption: Companies hesitate to build products on protocols owned by a single competitor because roadmaps reflect only that owner's priorities and the protocol could be discontinued. Housing standards like MCP and A2A inside the Agentic AI Foundation under the Linux Foundation removes that risk, giving enterprises the confidence to build on shared infrastructure without vendor lock-in concerns.
- ✓Working Groups Move Faster When Members Have Commercial Stakes: Unlike traditional foundations relying on volunteer contributors, Agentic AI Foundation working groups include engineers whose companies depend on the outcomes. PayPal, Stripe, Google, and Anthropic engineers collaborate on agentic commerce and identity standards because their products require resolution. Commercial urgency compresses timelines that would otherwise take years in conventional standards bodies.
What It Covers
Angie Jones, VP of the Agentic AI Foundation, explains how the Linux Foundation-backed nonprofit unites competing companies including OpenAI, Anthropic, Block, and Google to develop neutral open standards for agentic AI, covering MCP, agent-to-agent protocols, and global adoption challenges across enterprise and non-technical populations.
Key Questions Answered
- •Enterprise AI Adoption — The 1-9-90 Champion Model: To scale AI adoption across 3,500 engineers, identify the top 1% of enthusiasts and form a cohort of ~50 cross-functional champions representing every major repo type (frontend, backend, iOS, Android). Give them 30% of their time to experiment, then have them embed working techniques directly into repositories so all teammates benefit automatically without individual retraining.
- •Context Engineering Over Individual Training: Rather than teaching every developer prompt techniques, embed agent instructions, skills, and context files directly into the codebase. When engineers point their AI agent at a repo, the agent inherits the team's preferred behavior automatically. This approach separates the knowledge-building burden from the individual user, making adoption frictionless at scale across large monorepos.
- •Non-Technical AI Onboarding — Lead With Data Access, Not Automation: When introducing agents to finance, HR, or marketing teams, start with a concrete time-saving win: give them instant access to database records they previously had to request from another team. Bypassing multi-day report request cycles builds immediate trust. Once trust is established, employees naturally expand delegation to more core tasks at their own pace.
- •Neutral Standards Homes Accelerate Enterprise Adoption: Companies hesitate to build products on protocols owned by a single competitor because roadmaps reflect only that owner's priorities and the protocol could be discontinued. Housing standards like MCP and A2A inside the Agentic AI Foundation under the Linux Foundation removes that risk, giving enterprises the confidence to build on shared infrastructure without vendor lock-in concerns.
- •Working Groups Move Faster When Members Have Commercial Stakes: Unlike traditional foundations relying on volunteer contributors, Agentic AI Foundation working groups include engineers whose companies depend on the outcomes. PayPal, Stripe, Google, and Anthropic engineers collaborate on agentic commerce and identity standards because their products require resolution. Commercial urgency compresses timelines that would otherwise take years in conventional standards bodies.
Notable Moment
Despite being fierce public competitors, engineers from OpenAI, Anthropic, Google, and Block collaborate productively inside foundation working groups, setting aside corporate rivalries entirely. The MCP Discord server serves as a live example, with dozens of active working groups doing concrete protocol development across competing organizations simultaneously.
Episode Transcript
Welcome to the Practical AI podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, x, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm. Now onto the show. Hey. Welcome to another edition of the Practical AI podcast. I am your cohost going solo today. I'm Chris Benson. Daniel's not with me this time, but we have an excellent conversation coming up for you. With me today, I have Angie Jones, who is the vice president of the AgenTic AI Foundation, which I think is a super cool title to have, at a at a super cool name place. And I'm really looking forward to finding out more about it. Angie, welcome to the show. Thanks so much, Chris. So, like I said there in the intro, and I said it before before, the the show started and stuff, like like, if somebody in AI was looking for a a play like, the agent I mean, like, it is that is the coolest sounding, thing you can have. And, but before we dive too far into the foundation, I'd really like to kinda hear, like, how does someone like like, how do you develop in your career so that you end up doing that? Like, could you tell us a little bit about your background, because that's a that, you know, that's, like, that's one of those things if you if you went and just said something to like, I work with tons of people working on iGenTech AI, but, like, leading the foundation of people. So I'm just curious, like, how do you get to that point? Yeah. So I'm I'm your traditional techie. So I've worked as an engineer for a couple of decades. So, you know, at places like IBM and Twitter, and the last role was at Block in a engineering leadership capacity. And so in that role, one of my tasks was to, basically teach the entire company, 12,000 people, how to use AI agents. And this is as I'm learning myself because, I mean, there's no book for this, you know, right out the gate. And, that was early, like, 2024. So a lot of this stuff was brand new. It was not even common in tech, let alone in others' verticals. Right? And so, I also like lead developer relations. And so a big part of my role is helping developers worldwide understand new technologies and how to use them. And so, our company Block created, like, this internal AI agent, named Goose. And, Goose, we were the developers were using Goose …
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Books, tools, and gear mentioned in this episode
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Tools
“SPONSORS: Prediction Guard (https://predictionguard.com/practicalai)”
“develop neutral open standards for agentic AI, covering MCP, agent-to-agent protocols, and global adoption challenges”
“SPONSORS: Framer (https://framer.com/practicalai)”
other
by Linux Foundation
“the Linux Foundation-backed nonprofit unites competing companies including OpenAI, Anthropic, Block, and Google to develop neutral open standards for agentic AI”
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