AI Agents Are Here, But They’re Only as Smart as the Data They’re Built On

Derek Slager, co-founder and CTO of Amperity, explains why AI agents are only as smart as the data they’re built on. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

AI agents are transforming how work gets done. From digital concierges and customer support bots to marketing assistants and supply chain optimizers, vendors are racing to launch next-generation tools that analyze and act on data in real-time. But as their capabilities grow, one critical blind spot threatens their reliability: the fragmented, inconsistent data foundations they’re built on.

Too often, AI agents are launched on fractured, siloed, and usually outdated data, creating gaps or inaccuracies. Without a solid data foundation, agents can struggle with everything from customer relevance to regulatory compliance. When decisions are being made and executed in real-time, intelligent agents need the best data available to deliver reliable outcomes.

AI Agents Need Clean, Connected Data

AI agents are designed to process and act on large volumes of data in real-time. However, these results are limited when customer information lives in disconnected systems, such as CRMs, point-of-sale platforms, loyalty databases, advertising tools, and service systems, which are all structured and updated differently.

For example, AI agents used for customer engagement must be able to reliably trigger a workflow. But if the agent isn’t working from a solid and accurate data foundation, it might surface an irrelevant offer, ignore a service issue, or send a communication to the wrong customer. Although AI agents are intended for optimization, they become a liability if they use inaccurate data.

A connected data layer is critical, but connection alone isn’t enough. Agents need a unified, contextual view of each customer to act intelligently, something only robust identity resolution can provide.

AI Agents are Changing the Identity Resolution Equation

Identity resolution has long been considered a prerequisite for customer engagement and hyper-personalization. But that’s no longer the whole story. With the emergence of agentic AI, identity resolution is being reimagined as a process that can be actively fueled and accelerated by intelligent agents.

Instead of depending on rigid matching rules or static configuration, AI agents can now overhaul identity resolution directly. They can ingest high-volume datasets across systems and stitch together fragmented customer records into unified profiles. These agents can analyze signals like names, behaviors, email addresses, and more using machine learning to determine which records belong to a single individual with unprecedented speed. This shift drastically improves the accuracy and agility of identity resolution, explaining the “why” behind two records matching and continually learning from new customer patterns. This means less manual tuning, fewer errors, and faster adaptation when data evolves.

AI agents help unlock cleaner profiles that other systems can trust. That clarity powers every other agent tasked with decision-making, personalization, or automation across the enterprise. Introducing identity resolution agents marks a turning point: businesses no longer need to choose between speed and precision. They can have the best of both worlds, at scale and in real-time.

Compliant Data is Still an AI Imperative

As generative AI and automated agents become increasingly integrated into customer-facing experiences, compliance is a critical component. Privacy regulations, consent frameworks, and internal governance standards must be embedded directly into the data layer powering these tools. When an AI agent accesses a customer profile, it must apply real-time opt-outs, data use regulations, and brand-specific compliance policies. That means customer consent and data governance must be encoded into every identity and every action.

Failure to do this puts companies at risk of reputational damage, regulatory fines, and customer attrition. A compliant data design creates the conditions for trusted, scalable, and ethical AI deployment when done right. Organizations must invest in systems that can manage data and enforce policy across every use case, especially those involving automation.

Responsible AI Agents Start at the Foundation

Responsible AI starts with how data is collected, cleaned, and made accessible. Without that foundation, any downstream AI tool will operate with limited context and unreliable inputs. IT and data leaders play a pivotal role in this shift. As stewards of the infrastructure and data strategy, they can determine whether AI agents will enhance the business or impact customer trust. They must now prepare data for intelligent use across the enterprise. That means designing for real-time access to customer profiles, enforcing compliance and governance controls, and building scalable ecosystems that integrate seamlessly with the systems where agents operate. This baseline starting point turns AI agents into real operational assets.

Bridging the Gap Between Innovation and Readiness

AI agents powering identity resolution will transform how companies engage customers and make decisions. But without clean, contextualized, and compliant data, they risk adding complexity and confusion instead of optimization and productivity.

The path forward is clear: invest in a modern data foundation that supports fast, accurate identity resolution and real-time enrichment. With the proper groundwork, AI agents can move from promise to performance, confidently driving automation, personalization, and innovation.