By Derek Slager, co-founder and CTO, Amperity
AI agents are everywhere. From virtual customer service bots to marketing automation tools, vendors are racing to launch agents that promise faster service, smarter decisions and greater adaptability. For IT and data leaders, these tools represent a new generation of intelligent systems-ones that can boost productivity across the organization.
But behind every smart agent is something even more important: smart data.
Even the most advanced AI agent is only as good as the data it’s built on. Fragmented, outdated or inconsistent information can make the best tech unreliable. And yet, that’s the starting point for many organizations today.
To work effectively, AI agents need data foundations that are accurate, connected and governed. Without them, outputs become unreliable and trust breaks down. Meanwhile, expectations keep rising. According to McKinsey, 71% of consumers expect companies to deliver personalized interactions. Meeting those expectations starts with data that’s as intelligent, unified and real-time as the experiences agents are expected to power.
Consistency is Key
At their core, AI agents use data to make decisions across systems, based on constantly changing variables and conditions. However, if the underlying customer data is spread across disconnected tools, fraught with duplication or siloed in different formats, the agent is doomed to be ineffective.
Consider a customer engagement agent that’s been tasked with triggering personalized customer offers for a retailer. If it doesn’t have access to up-to-date purchase history, communication preferences or support tickets, it may surface the wrong offer, send an offer to the wrong person or break a compliance rule. These are everyday scenarios when data is inconsistent or misaligned across systems.
For agents to perform effectively, they need consistent, reliable data. That means not just centralized data storage, but also ensuring data is unified, up-to-date and accurate across every application they touch.
Why Identity Resolution Matters
One of the biggest blockers to clean data? Unresolved customer identities. Many businesses have the same individual represented under multiple names, emails or device IDs across systems. Humans might spot and adjust for those inconsistencies-agents won’t.
This is where modern identity resolution comes in. AI-powered systems can analyze behavioral and identifying signals across datasets, stitching fragmented records into a single, unified profile. These identity resolution agents learn over time, adapting as behaviors shift and profiles evolve, boosting accuracy while reducing manual work.
When agents operate on unified identities, they can act with far more confidence, reducing friction and enabling more relevant, effective interactions.
Governance at the Speed of AI
Agents increasingly operate in real time, and that includes decisions involving privacy and compliance. But most governance systems weren’t built for that speed.
Too often, consent policies and suppression rules are bolted on after agent deployment, introducing delays and risk. In an agent-first world, governance needs to be embedded from the start.
That means building consent, access and usage rules directly into the data layer. When agents can “see” governance as part of the data itself, they can act safely without slowing down.
Don’t Bolt Agents onto Broken Infrastructure
It’s tempting to adopt AI agents quickly, especially when the innovation cycle is moving fast and competitive pressure is high. But many companies are trying to bolt agents onto brittle, outdated infrastructure.
To deliver real value, agents need systems that are ready. That means enabling real-time data activation, dynamic identity resolution and embedded governance from the ground up. It also requires cross-functional alignment, so IT, marketing, CX and ops are all working from the same playbook.
When these foundations are in place, agents can make decisions that reflect the full customer context-and drive outcomes you can trust.
Three Steps to Smarter Data for Smarter Agents
To close the gap between AI promise and AI performance, prioritize data readiness. Start here:
- Use AI-powered identity resolution – Resolve duplicates and unify records in real time, so every agent-whether in marketing or customer service-has access to consistent, complete profiles.
- Enable real-time data access – Agents can’t wait for batch jobs. Build pipelines that support low-latency ingestion, transformation and delivery.
- Make governance native to your data – Bake privacy, consent and usage policies into the data itself, so agents operate safely and autonomously.
When the Data is Right, the AI Follows
AI agents are powerful-but only if they’re built on trustworthy data. The more accurate, current and governed your data, the more effective your agents will be.
As adoption accelerates, it’s time to shift the focus from flashy interfaces to foundational intelligence. The best agents in the world can’t make up for missing, messy or misgoverned data. But when the data is right? That’s when the magic happens.
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ABOUT THE AUTHOR
Derek co-founded Amperity to create a platform that would give marketers and analysts access to accurate, consistent and comprehensive customer data. As CTO, he leads the company’s product, engineering, operations and information security teams to deliver on Amperity’s mission of helping people use data to serve customers. Prior to Amperity, Derek was on the founding team at Appature and held engineering leadership positions at various business and consumer-facing startups, focusing on large-scale distributed systems and security.
