The automation conversation in title insurance has moved quickly. A few years ago, the debate was whether robotic process automation was worth the investment. Today it has jumped ahead to agentic AI, systems that can learn, make autonomous decisions, and act without step-by-step human instruction. Vendors are leading with it. Conference sessions are devoted to it. And a growing number of title company executives are asking whether they need it.
The answer, according to the team at TrueFocus Automation, is nuanced. Agentic AI is not fiction, and it is not irrelevant to title operations. But giving it autonomous control over regulated, high-stakes workflows in 2026 is a decision that deserves far more scrutiny than the vendor pitches suggest.
What Agentic AI Actually Is, and What It Is Not
To understand the debate, it helps to start with what distinguishes agentic AI from the automation tools that title companies already use. Traditional RPA bots follow a fixed workflow. They are trained on specific steps, and when something falls outside those steps, the bot stops and flags the exception for a human to handle. Agentic AI works differently. It can make decisions on the fly, adapt to new inputs, and take actions without being explicitly programmed for each scenario.
That flexibility is genuinely useful in some contexts. But in title insurance, where a misjudgment in a search result or a document categorization can contribute to a claim down the line, the same flexibility creates a different kind of risk. You lose visibility into why the system made the decision it made. And if something fails, you may not know it failed until the damage is done.
Sridhar Loganathan, COO of TrueFocus Automation, frames the tradeoff directly. With conventional RPA, workflows are deterministic; if a step breaks, it’s easy to isolate and fix. Agentic AI introduces autonomous decision-making, which makes tracing the exact logic behind a failure far more challenging. “The risk with pure Agentic AI is unpredictability when an autonomous system makes an error; finding the root cause is like looking into a black box,” Loganathan explains. “In our architecture, we maintain a modular approach. If there is an issue in one specific part of the process, we can isolate and fix that single component while the rest of the operation keeps running smoothly.”
TrueFocus is not dismissing agentic AI. The company is pursuing opportunities to use it in parts of its workflow where it adds genuine value. But the distinction between using it selectively and handing it control of an entire title search or closing process is significant, and that distinction is getting lost in the current vendor landscape.
Why Title Insurance Demands Managed AI, Not Unsupervised Agents
The case for caution is not abstract. Title insurance is a regulated industry with real liability attached to errors. A missed lien, a miscategorized document, or a failure to catch a chain-of-title defect carries financial and legal consequences that extend well beyond the inconvenience of a bad software output. The industry has spent decades developing processes, oversight structures, and staffing models to manage exactly that kind of risk.
Jimmy Lewis, co-founder of TrueFocus Automation, points to where clients actually stand. Title companies are risk-averse by nature, and many have only recently become comfortable with the idea of automation as a complement to their existing staff. The industry took years to accept outsourcing. Accepting fully autonomous technology is a further step that requires the technology to have proven itself in a title-specific context first. Pushing companies toward autonomous AI before that proof exists does not accelerate innovation – it transfers risk from the vendor to the client.
As Lewis puts it: “If you jump from in-house to fully autonomous technology, you need to be very careful about what that could do to your workflows.”
What the Right Balance Actually Looks Like in Practice
Rather than treating automation as a single technology choice, TrueFocus uses a layered model that matches each tool to the work it handles reliably today. RPA manages the structured, repeatable parts of the workflow, retrieving records, navigating county websites, opening orders, and completing a variety of escrow tasks. AI handles document understanding, categorization, and data extraction, where pattern recognition outperforms rigid rule-based coding. Agentic AI can be introduced carefully in specific scenarios where its adaptive capabilities offer a genuine advantage, with controls in place to contain the impact if something goes wrong.
The result is a workflow where automation handles high-volume, low-judgment work efficiently, and the human examiner reviews output and makes the calls that require real expertise. That model produces a concrete productivity gain: Title Hunter clients completing searches in fifteen to twenty minutes instead of sixty, without removing accountability from the process.
Loganathan describes the underlying philosophy as maintaining clear boundaries: knowing where agentic AI adds value, how much autonomy to extend, and what controls need to stay in place. “We should not move away from control entirely,” he says. “You need to be clear about where to leverage agentic AI, how much to leverage, and not give away total control of your solution.”
What to Ask Any Vendor Making Agentic AI Claims
For title companies evaluating vendors, the marketing language around agentic AI warrants direct follow-up questions. What happens when the system makes a wrong decision, and how is that identified? Does the workflow include human review at meaningful checkpoints, or is the appeal of the product that humans are removed from the loop entirely? How does the system handle edge cases, and who is responsible when an edge case produces a bad outcome?
The honest answer to most of these questions, from any vendor willing to give one, is that agentic AI at scale in title operations is still maturing before it can be reliably deployable without human oversight. The vendors worth working with are the ones who say that plainly, rather than selling confidence they cannot back up.
The technology will get there. The argument is not against agentic AI. It is against deploying it in a way that trades oversight for novelty before the risk profile is understood.
Sridhar Loganathan is the co-founder of TrueFocus Automation, a specialist in RPA and AI-driven workflow automation for the title insurance, mortgage, and real estate industries. TrueFocus has developed 840+ automation bots supporting more than 2,500 workflows and has returned over 1.3 million production hours to clients.
This article is intended for informational purposes only and does not constitute legal, financial, or investment advice. The views and opinions expressed herein reflect those of the individuals quoted and do not represent an endorsement of any company, product, or service mentioned. Readers should conduct their own due diligence and consult qualified professionals before making any investment decisions.
Disclosure: Individuals or companies mentioned may have a commercial relationship with KeyCrew.
