A.Team

We're thrilled to announce that Angelique Bellmer Krembs, A.Team’s tenured CMO in Residence, is officially the Chair of our CxO Network’s AI CPG Committee! Angelique is the trifecta: CMO, Brand Advisor, and Board Director — she has decades of experience from brand, strategy and operational roles at PepsiCo, as well as years partnering with dozens of CPG and CPG Retailer giants like P&G, General Mills, Johnson & Johnson, Target, Kroger, Walmart... just to name a few. We asked her how top CPG teams are using AI to create value across their portfolio today, here's what she said: “From what I've been privileged to see through the work of A.Team, the future belongs to organizations that treat AI not as an efficiency tool, but as a force multiplier for human judgment, creativity, and storytelling at scale. AI’s greatest enterprise impact is its ability to elevate how teams think and work together: being the ‘glue’ of knowledge, understanding and action across teams at a scale never before possible.” For Angelique, “It's always been about the People - the super smart, courageous, visionary people I've met in and through A.Team's Network are people that sit on the cutting edge of the fast-changing world of AI - AND they are the most trustworthy and fun humans, making this crazy ride absolutely delightful.” Angelique, the feeling is mutual —we are thrilled to have your leadership in the CxO Network!

🌪️ We've been talking about "leading through digital disruption" since banner ads. Now? It's not just disruption—it's a tsunami. 🤝 Join an exceptional panel of marketing leaders from GTAN (the Gail Tifford Alumni Network) as they get real about leading when the ground won't stop shifting. These are executives currently steering high-growth brands through AI transformation, potential trade wars, midterm election volatility, and whatever fresh hell emerges between now and April. Hear from Tara Walpert Levy, Google + Casey DePalma McCartney, Unilever + Lauren Weinberg, Supergoop! + Julie Y., A.Team + Amani Duncan, My Code + Gail Tifford, True Search + more Digital Ascendant is returning to NYC with a full, engaging day of programming on April 14th! 🔥 Request invite + get info: https://lnkd.in/gbjSEKUD #togetherwerise #mediadisruptions #digitalascendant #ascendantnetwork

Companies are layering automation onto broken workflows rather than rebuilding operations around AI as a core design principle. That misalignment is one reason more than 40% of agentic AI initiatives are projected to be cancelled by 2027. To address this, A.Team AI/ML builder Berk H. (former Brown ML researcher, ex-AWS), walked our network through a pattern gaining traction to address this: The Ralph Wiggum loop. The idea is simple: you write a spec with verifiable stopping criteria, run the agent inside an external loop, it completes a task, logs progress, then the loop resets the agent to avoid unbounded context growth before starting the next one. You define specific capabilities, give the system a goal, and let it iterate—task by task—until completion. Why does this matter? When an agent runs long, multi‑step workflows without boundaries, context fills up, the model compresses information, and decision‑making degrades. Ralph forces discipline: narrow task scopes, explicit completion criteria, and periodic resets that keep context under control. TLDR; This could be the difference between agentic systems that scale in production and those that quietly get shelved. What makes this orchestration hard for most teams is intent–agent alignment: the gap between what you intend and what the agent actually does. Because the agent is inherently ignorant, persistent, and optimistic, achieving reliable outcomes depends less on code and more on having the expertise for high‑quality, continuously refined specifications. Thinking about how to apply this in your orgs systems? These sessions are part of how we keep A.Team's builder network sharp on applied AI, to learn more visit: https://hubs.la/Q048VTfq0

Thank you ShiSh S. and the Microsoft team for the icredible insights on what the shift to agentic AI actually looks like in the enterprise — and how A.Team is shaping that path! We're excited to be building alongside the Microsoft for Startups Pegasus community to help Fortune 500 retail and brand marketing teams close the gap between insight and action with agentic AI. 🚀

What happens when your AI agent starts to act in ways it's not supposed to? Enterprise pilots often fail when there's no clean boundary between what the AI recommends and what actually gets executed. Protocol-first design fixes this by significantly reducing risk and making failures observable. The idea is: You define the rules of what can and can't happen in your system before you integrate the AI. The model operates inside those boundaries, not on top of them: reading structured observations, reasoning over multiple variables, and producing different recommendations based on changing conditions all inside the boundaries you’ve set. In a recent AI/ML Guild session, Ric A., a PhD candidate in AI and an AI Guild Lead in the A.Team network walked builders through how his team does this in practice, with a three layer architecture: → Decision layer — where the LLM reasons and recommends. It sees structured data (not raw system state), produces recommendations with estimated confidence scores, and generates human-readable explanations for why it's recommending a particular action. → Validation layer — deterministic rules that check whether the recommendation is actually allowed. Things like: is this action too similar to the last one? Does it violate a frequency limit? Is the confidence score above threshold? No AI involved here — just hard rules. → Execution layer — where permitted actions actually happen. The LLM never touches this layer directly. It never signs transactions, never moves funds, never modifies state, etc. With this approach, the evaluation changes from "was the model smart?" to "did the system preserve its rules?" Which means, you can actually validate if your system is behaving reliably, and have a framework to catch it when it doesn't. Wondering how to apply protocol first design to your orgs AI system? These sessions are part of how we keep A.Team's builder network sharp on applied AI, to get in touch visit: https://hubs.la/Q048rP-V0

The gap between insight and action is where enterprise value gets lost. A.Team builds custom agentic AI systems to close it — connecting siloed data into compounding intelligence that surfaces inside the tools enterprise teams already use. Copilot. Teams. PowerPoint. Excel. The places where decisions actually happen. Proud to join the Microsoft for Startups Pegasus Program! Excited to embed intelligence where it matters most.