The software world spent decades perfecting click-based user interfaces (UIs). Users learned to navigate complex workflows through buttons, menus, and forms.

But with that familiarity came a new set of frustrations: hunting for the right button, clicking through multiple screens for simple tasks, getting stuck when the interface doesn't match their mental model.

AI agents promise to solve these friction points by letting users express their intent directly. No more hunting through menus—just tell the system what you want.

But here's where most companies are fumbling: They're transforming simple things, like button clicks, into conversational form rather than tackling the complex, multi-step workflows where agents can actually shine.

Building effective AI agents isn't about finding the shiniest LLM, it's about having your data and SaaS systems in order. Most companies are trying to build the penthouse before they've poured the foundation, and wondering why everything collapses. Here's how to actually build an AI stack that works.

3 layers of data for a high-performing AI stack

Think of AI readiness as a three-layer cake. Most companies are trying to build the fancy frosting (the agent interface) without bothering to bake the actual cake underneath. Here's how to build a foundation that won't crumble:

Layer 1: Data

Data from your SaaS stack, data warehouses/lakes, and Infrastructure as a Service (IaaS) is raw material for your agents. Before you do anything else, ask yourself: 

  • What data do you actually have versus what you think you have?
  • Is your critical information scattered across 47 different tools? (Spoiler: it probably is.)

Can you connect the dots between user behavior and business outcomes?

This isn't just about having data. It's about having the right data in the right format. Your agent needs context to be useful, and context comes from connecting disparate data sources in meaningful ways.

Mapping agents to strategic priorities

Start by cataloging your data landscape:

  • Your proprietary data: The crown jewels that only you have—user interactions, product usage patterns, customer communications
  • Integration data: The connective tissue from your tech stack—CRM records, support tickets, analytics events
  • External context: Industry benchmarks, market data, regulatory information

Mapping isn't just about inventory. You need to understand the relationships between these data sources and identify the security and privacy landmines you'll need to navigate. How you design this data foundation will determine what's possible in the layers above.

Layer 2: Insights

Raw data is just noise until you extract meaningful patterns from it. This layer is about developing the analytical capabilities to spot trends, predict behaviors, and surface insights that humans would miss or take too long to find manually.

Think about Netflix's recommendation engine. They don't just know what viewers watched, they understand viewing patterns, seasonal preferences, how far into shows people drop off, and what combinations of genres work for different user segments. That's the insights layer in everyday life. 

Each company has its own patterns and interesting behaviors, which is where you can differentiate. For your business, this might mean:

  • Usage patterns that reveal hidden workflows and pain points
  • Behavioral signals that predict churn, conversion, or engagement
  • Conversation topics that emerge from support interactions or sales calls
  • Industry-specific patterns like fraud detection in banking or quality issues in manufacturing

Every type of data contains interesting patterns if you know how to look. Your job is to find the signals that matter for your specific business goals.

Layer 3: Application 

This is where the magic happens. It’s also where most companies start, but it should be your final layer. Once you have a solid data infrastructure and pattern recognition capabilities from layers 1 and 2, you can build applications that actually solve problems.

Forget the customer service bot that just regurgitates your FAQ. Think bigger:

  • Proactive recommendations that surface relevant information before users ask (like Netflix suggesting your next binge).
  • Contextual guidance that appears at exactly the right moment in a user's workflow.
  • Agents that complete tasks, not just answer questions. Can agents take actions across multiple systems?

The application layer isn't limited to conversational interfaces either. Sometimes the best "agent" is an insight that appears on your dashboard or a notification that pops up when specific conditions are met.

You’re flying blind without agentic insights

Traditional software analytics tell you what users clicked, but not why they clicked it. Agent analytics are different. Users tell you their intent directly through natural language, creating unprecedented insight into what users actually want to accomplish.

When someone asks your agent, "How do I reduce customer churn?" You learn about their goals in a way that button clicks have never revealed. When they follow up with, "What about for enterprise customers specifically?" you understand how they're thinking about segmentation. This is valuable intelligence about user needs, feature gaps, and product direction, so you can get closer to your users and invest in software that’ll actually get used. 

Prepare your data foundation now, or play catch-up later. The companies winning with agents aren't necessarily the ones with the best AI teams—they're the ones with the best data infrastructure. While everyone else is debating which LLM to use, they're already connecting user intent to business outcomes.

Want to learn more about becoming a SaaS + AI powerhouse? Get a demo.