By Sridhar Sharma, President, Sagent
As AI resets mortgage servicing, software is opening up. The data underneath is unified and real-time. Compliance moves at the speed of policymaking, not the speed of PDFs. And perhaps most important, servicers now run their own AI agents alongside their vendors'. At Sagent, we call this BYOA – Bring Your Own Agents. Let's look at how this works, and how it helps servicers win.
Servicers Are Building Their Own AI. Their Tech Stack Must Let Them.
Every large servicer I talk to is doing some version of the same thing. They're standing up internal AI agents on Palantir Foundry, on Azure, on whatever framework their data and platform teams are best at. Those agents are getting good. Some are very good. The question isn't whether servicers will have their own AI. It's whether their servicing platform will treat that AI as a partner or a problem.
The right answer is partner. We built Dara with an MCP server layer so customers can bring their own agents – BYOA – and have them talk to Dara natively, agent-to-agent. A servicer's internal call-center agent can pull live loan context. Their compliance agent can walk through a PMI cancellation. Or they can use ours. Or they can mix. The platform doesn't care. What it cares about is that whatever runs gets clean, governed, real-time servicing data and a clear path back through human-in-the-loop oversight.
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" – doesn't fit a world where servicers are becoming AI shops themselves.
The 3 Layers Needed For AI Success
The mistake most AI-in-servicing conversations make is pretending every servicer is at the same place. They're not. The more useful conversation is built around three layers – and being honest about which one matters for a given customer right now.
The foundation is data access: read-only, governed, natural-language queries against the servicing system. If a customer's internal agents are already mature, this is often all they need from a vendor, 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 vendor owns the correctness of each step.
The top layer is full workflows exposed as a single call. An agent says "terminate PMI" and the entire chain executes, with sequencing, state, and exception handling productized end-to-end.
Same data. Same product. Three different sales conversations depending on where the customer actually is. The honest version of vendor-customer dialogue in 2026 doesn't start with a demo. It starts with one question: which of these three layers do you actually need today?
Compliance Goes From Weeks To Hours. And Yes, It's Cool.
Because of its importance, compliance has for too long been a human-review bottleneck. AI on unified data changes that. The average large servicer is on the hook for north of 8,800 rules, GSE and agency requirements, investor overlays, and insurer guidelines, and that number only ever moves in one direction. The legacy workflow – ingest a change, schedule meetings across product, engineering, and compliance, dig through code, publish a PDF – measures in weeks.
We're closing that loop in hours. Dara continuously scans CFPB, Fannie Mae, Freddie Mac, FHA, VA, USDA, OCC, and FDIC for changes. It maps each one 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 exactly what to do" collapses. This kind of AI-powered native compliance is transformative to servicer operations.
From Systems Of Record To Systems Of Action
Everything above points to the same shift. Servicing platforms have spent decades being systems of record. The platforms that matter over the next ten will be systems of action – proactive, CTA-driven, AI-native where it helps, traditional SaaS where it should be, and fully auditable everywhere. Every action logged. Every decision traceable. No black boxes, no matter whose agent did the work.
That's the version of the AI reset we continue to build at Sagent, and the version America's $15 trillion mortgage servicing sector actually deserves. The architecture is here. The early movers are already running on it. And their operational lift is accelerating.
