--- title: Scaling Agentic AI: Lessons From the Field source_page: https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/scaling-agentic-ai canonical: https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/scaling-agentic-ai format: text/markdown date: 2026-10-10T04:01:00.000Z description: Scaling agentic AI means more than automation. A lesson from the field on how one risk team built agents that grew revenue while cutting loss rates… --- **Quick links:** [Human page](https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/scaling-agentic-ai) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/blog) · [Demo](https://www.sardine.ai/demo) --- # Scaling Agentic AI: Lessons From the Field **Published:** 2026-10-10T04:01:00.000Z Scaling agentic AI means more than automation. A lesson from the field on how one risk team built agents that grew revenue while cutting loss rates… A few weeks ago I walked through the framework for the AI transformation journey. This conversation with Lalitha Rao, Chief Risk Officer at Imprint, is the natural next step. It is a real, in-the-field look at what it actually takes when a team commits to scaling agentic AI past the theory stage. In the nine months since her risk team started building out their agentic AI infrastructure in earnest, they’ve grown their loan book by 150%, increased revenue by 2.5x, and actually lowered loss rates on newer cohorts. All without cutting a single headcount. Lalitha’s team didn’t treat this as a cost-cutting exercise, they treated it as a way to create more value for Imprint's members and partners. And the results speak for themselves. If you’re further along in your own AI transformation journey and want a real example of what scaling agentic AI actually looks like once the framework meets the field, this is the conversation. What you'll hear in this episode: Why Imprint's centralized AI platform, built by their CTO's team, turned out to matter more than any individual agent they've built How ARIA, an autonomous credit policy agent, scours bureau and internal data to find early signs of portfolio deterioration and pushes the efficient frontier outward instead of just optimizing it Why agentic AI unlocked something a SQL query or a standalone model couldn't, digesting multiple data sources in different formats into one coherent, repeatable workflow How a residual mining agent continuously finds model errors, builds new features, and retrains models using out-of-time testing to guard against overfitting Why Imprint never measured success by headcount reduction, and what they built instead with the team's freed-up time Inside Project Shield, a suite of ten fraud agents that runs for six hours, costs about $300 in tokens, and saves hundreds of thousands of dollars in fraud losses How a shared skills repository and clear data ownership let a non-engineering risk team build and maintain production-grade agents Lalitha's honest build versus buy framework, and the specific vendors, including Sardine, LexisNexis, and Proof, Imprint still relies on The real hiccups Imprint hit along the way, bloated proposals, false certainty in AI-generated reviews, and how they built guardrails to fix both You should listen to this episode if you: Are further along in the AI transformation journey and want a concrete look at what scaling actually requires beyond the initial framework Lead a risk, fraud, or credit team and are trying to decide which agentic AI use cases to prioritize first Are weighing build versus buy decisions for AI agents and want a clear mental model for where proprietary data changes the calculus Worry that scaling AI means shrinking your team, and want to hear a real counterexample Want practical detail on how to actually enable non-engineers to build and maintain production-grade AI agents ### Episode notes & key takeaways Building the agentic AI infrastructure first About 80% of the work behind scaling AI agents in risk is agentic AI data infrastructure, a centralized AI platform fintech teams can actually build on, with standardized data access and tooling. In Imprint's case that meant an MCP connection into Snowflake, Notion, Linear, and Slack, plus clear documentation on data lineage. An AI agent shared skills repository, peer-reviewed and version-controlled, is what let a non-engineer AI agent development effort succeed at all, turning the risk team into the company's second-heaviest internal AI agent adoption fintech story after engineering itself. That combination is really the full list of AI agent build prerequisites worth taking seriously before anything else gets built. What ARIA unlocks that other tools can't ARIA, Imprint's acquisition risk intelligence agent, functions as an autonomous credit policy analyst, scanning portfolio performance and bureau data in parallel across every partner program for agentic AI underwriting optimization, catching early signs of deterioration before they show up as delinquencies. A second, residual risk AI agent works in the background hunting for where models are failing, generating new features and handling agentic AI model retraining through out-of-time testing AI agents rely on to guard against overfitting. Together, these two didn't just move Imprint to a more optimal point on the agentic AI efficient frontier. They pushed the frontier itself outward. No headcount cut, better quality of life This was the point I think matters most. Imprint never framed AI agent deployment risk team work as an efficiency or cost-cutting project. Fraud reviewers who used to spend the first hours of every day clearing obvious false positives now spend that time hunting for genuinely new fraud patterns, a real gain in AI agent false positive reduction. The team's own reported AI agent quality of life improved, not despite the AI rollout, but because of it. Project Shield scales institutional fraud knowledge Imprint's fraud team built a suite of ten agents, called Project Shield, that runs for about six hours at a time, costs roughly $300 in tokens, and handles AI fraud rule generation that saves hundreds of thousands of dollars a year. The suite includes an agent that proposes new rules and features, and a separate agent built specifically to play skeptic, checking precision and false positive rates before anything reaches production. Early AI agent hiccups production issues, like AI-generated reviews that read as more confident than they should have, pushed the team toward real AI agent certainty measurement and AI agent explainability risk practices, exposing a certainty score and its underlying reasoning in every output so a human reviewer could see exactly where data was missing before making the final call. Build versus buy, and the bigger mindset shift Lalitha's framework for build vs buy AI agents is straightforward, build where proprietary data creates a genuine moat, and buy where a vendor already has the scale and depth to do it better, which is why Imprint still uses Sardine, LexisNexis, and Proof alongside its own in-house agents. Her closing advice ties it together, and it's really how to scale AI agents in risk without losing the plot. Most teams start this journey asking how to become more efficient at their current point on the efficient frontier, including early AI agent personalization fintech use cases. The bigger opportunity is shifting that frontier entirely, and staying loose enough about what you've already built, since the pace of change means almost nothing stays the best solution for long. ### Final takeaway If there's one thing I want every fraud and risk leader to take from this conversation, it's that scaling agentic AI was never really about replacing people. Lalitha's team didn't get smaller. It got the chance to do work that actually matters, spotting new fraud patterns instead of clearing false positives, thinking about credit strategy instead of assembling weekly reports. That's not a side effect of doing this well. It's the whole point. Resources & links Not ready to stop the conversation about my, and hopefully your, favorite subject? Subscribe to The Saturday Fraud Strategist newsletter. Connect with Lalitha Rao | LinkedIn Chief Risk Officer, Imprint Connect with Chen Zamir | LinkedIn Host of The Saturday Fraud Strategist Helping fintechs build smarter fraud defenses Co-author of “The Fraud Fighter’s AI Playbook”