Experian builds AI agents with trust at their core
Experian is developing AI agents capable of operating at scale within financial services—but only if those agents earn the trust of customers, regulators, and employees. For Gaurav Arora, head of generative AI and automation at Experian UK & Ireland, the core challenge lies not in technical execution but in governance. The goal is to embed compliance, transparency, and human accountability into AI systems from their inception rather than as an afterthought.
The company’s internal platform, ProductMate, originated as a tool for generating user stories but has since expanded into product development and service operations. Today, thousands of employees depend on it—not because it was built as an afterthought, but because trust was central to its design from the start.
Arora emphasizes that the transition from AI experimentation to widespread adoption in fintech has occurred rapidly. The critical question for firms is no longer whether they can build AI models, but whether those models can be deployed in ways that satisfy regulators, customers, and internal teams. ProductMate addresses this by treating AI not as a standalone tool but as an extension of the workforce—subject to the same governance standards as human colleagues.
AI Agents Need Governance by Design
A key principle underpinning the platform is that scaling AI agents depends not on the model itself, but on the foundation supporting it. Every agent in ProductMate operates with its own identity, controlled access to systems of record, full action observability, and continuous evaluation, not just a one-time compliance check before deployment. This structure eliminates repetitive work across teams. While other firms may repeatedly resolve the same compliance or integration issues, Experian’s agents inherit these safeguards automatically from day one.
The platform now supports a portfolio of live agents, each assigned a named owner and a dedicated support team. Arora observes that agents lacking clear ownership rarely survive in production environments. These agents handle everything from requirements gathering to governance checks, all while adhering to the strict standards of a regulated industry.
Arora rejects the notion that innovation and compliance represent opposing forces. In fact, the two reinforce each other. By integrating governance into the platform’s design, rather than layering it on afterward, Experian accelerates deployment timelines. Two of the most valuable agents automate compliance tasks: one prioritizes security vulnerabilities, while another identifies missing approvals. Proper governance does not slow progress; it enables it.
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Trust Grows When AI Solves Real Problems
The most significant obstacle was not technological but behavioral. Early training sessions achieved high completion rates, yet actual usage remained low. Adoption only increased when agents became embedded in daily workflows, directly solving problems teams already faced. As Arora states: “Training is an event. Capability is a habit.” Trust grows when employees can see how an agent operates and intervene when errors occur. Every action is logged, traceable, and linked to a human accountable for the outcome. The distinction between AI and human oversight is not assumed; it is explicitly documented.
The technical backbone of ProductMate relies on Amazon Bedrock AgentCore, which provides the runtime, identity management, and observability layers. This allows engineers to focus on defining an agent’s knowledge, permissions, and success metrics rather than rebuilding infrastructure. A new domain-specific agent can now be built in days and deployed across regions within weeks.
For teams, the impact is immediate. Engineers shift from execution to judgment, evaluating whether an agent’s output is correct and, if not, how to improve it. Testers evolve into quality strategists, while service engineers analyze root causes instead of handling ticket triage. Product managers transition from research to drafting specifications in single sessions.
Unified Platform Ends Fragmented AI Workflows
Experian’s shift from isolated AI projects to a unified platform required overcoming fragmented workflows across its teams. Initially, each department, whether product managers, testers, or service engineers, used AI tools in silos, leading to redundant integrations and inconsistent governance. The ProductMate team addressed this by building a shared infrastructure with standardized knowledge bases, system connections, and evaluation frameworks. This horizontal platform ensured every new agent, regardless of function, could rely on the same foundational safeguards.
The most persistent barrier proved behavioral. The turning point came when agents were integrated into daily workflows, resolving specific pain points, such as drafting test cases or flagging missing approvals, without requiring extra effort from employees. For example, one agent now automatically categorizes service tickets by priority, reducing engineers’ triage time. This seamless integration transformed AI from an abstract concept into a collaborative tool, directly addressing manual tasks that previously frustrated teams.
This reallocation of effort has produced measurable results. A governance team previously overwhelmed by manual approval tracking now uses an agent to auto-flag missing signatures or evidence. Meanwhile, product development cycles have accelerated as agents pre-populate requirements documents with structured data. The shift does not eliminate human judgment; it redirects it toward higher-value decisions. Where engineers once spent hours on repetitive work, they now spend minutes reviewing an agent’s output, correcting errors, or refining parameters. The limiting factor is not labor but the ability to articulate what AI should achieve and how success will be measured.
