By Sridhar Sharma, President, Sagent
AI is creating new opportunities in mortgage servicing. Servicing platforms are evolving from being just the 'system of record' to natively enabling outstanding context-aware customer experience in real-time. By helping unify your data no matter where it sits. By allowing you to wire and rewire your workflows and automations in real-time, anytime. Take compliance, for example – regulators and policymakers expect quick action. Beyond a point, they don't (and shouldn't) care about legacy system bottlenecks. And in response, we see many servicers building their own AI agents to run alongside (or around) their core platforms. At Sagent, we call this BYOA – Bring Your Own Agents. Let's look at how this works, and how it helps servicers win in high-stakes areas, like delivering on compliance and improving the customer experience.
Servicers Need a Tech Stack Which Supports AI (both ways)
Every large servicer I talk to is doing some version of the same thing. They're standing up internal AI-agents using frameworks their technology teams are best at. And those AI-agents are getting good. The question isn't whether servicers will have their own AI-agents. It's whether their servicing platform will treat that AI as a partner, or as a problem.
The right answer is partner. We built Dara with an MCP server layer so our customers can bring their own AI-agents – BYOA – and have them talk to Dara natively, AI-agent-to-AI-agent. A servicer's internal call-center AI-agent can pull live loan context and interrogate and analyze loan events within Dara. Or walk through a multi-step PMI cancellation process end to end. Alternatively, Servicers can use native Dara AI-agents with human in the loop (HITL) oversight. Or they can mix. The Dara platform is designed to give the Servicer flexibility. What Dara enables is direct access to (auditable) real-time servicing data, and low-to-no-touch process automations with HITL triggers to handle exceptions as they occur. And the result is a faster, simpler experience for the end customer – which is the key to building relationships for life.
This is what the AI reset in mortgage servicing looks like in practice. Today's too-common vendor pitch – "just use our AI, we've got it all covered" – may not fit a world where servicers are becoming AI shops themselves.
The 3 Layers Needed for AI Success
Not every servicer is at the same place in terms of their evolution on the AI journey. Servicers just starting to experiment with AI, or those looking to use AI to fix gaps in their legacy core systems, might be better off leveraging Dara's native AI platform. Servicers building AI-agents to drive deeper efficiencies or differentiated experiences might benefit from interfacing with Dara's bring-your-own-agent ecosystem.
The more useful conversation involves thinking through three layers of execution – and being honest about which one matters for a given customer right now.
The foundational layer is data access: read-only, governed, natural-language queries against the servicing system. If a customer's internal customer service AI-agents are already mature, this is often all they need from a partner, and it's a perfectly legitimate place to start.
The middle layer is process automation – single-step actions like submitting a curtailment, requesting a valuation, approving a PMI termination – where the partner owns the correctness of each step.
The top layer exposes full multi-step/multi-day workflows as a single call. An AI-agent says "terminate PMI" and the entire chain executes, with sequencing, state, and exception handling productized end-to-end, producing the swift and simple outcome your customer expects.
Same data. Same product. Three different conversations depending on where the servicer actually is. The pragmatic version of partner-customer dialogue in 2026 doesn't just start with a demo. It starts with asking which of these three layers do we need to improve today?
Compliance Goes from Weeks to Hours
Because of the severe consequences of mistakes, compliance has for too long been a human-review bottleneck. AI on unified data can change that radically. The average large servicer is on the hook for north of 8,800 rules, including GSE and agency requirements, investor overlays, insurer guidelines, and federal and state legislation --and this number is only growing. The legacy workflow – read about a change, schedule discussions across product, engineering, and compliance, dig through code, write and publish a new policy – measures in weeks.
With Dara, we're now closing that loop in hours. Dara continuously scans CFPB, Fannie Mae, Freddie Mac, FHA, VA, USDA, OCC, and FDIC for changes. It dynamically maps each change to the specific platform features it affects, summarizes the source, and links back to it. Humans still own the call on what changes and when. But the time from "regulator moved" to "we know what to do" collapses. This kind of AI-powered native compliance is transformative to servicer operations.
From Systems of Record to Systems of Action
Historically, servicing platforms have been considered systems of record. The platforms that matter over the next ten years will be systems of action – cloud-scale, optimized for AI, transparent, modular, scalable, and most importantly, fully auditable. This means every action logged. Every decision traceable. No black boxes, no matter which agent did the work.
That's the AI reset we've built at Sagent, and the version America's $15 trillion mortgage servicing sector deserves. The architecture is here. The early movers are already running on it, and the operational lift is only going to accelerate from here.
