Doing Trust Falls with AI: Agent Adoption Starts Long Before You Think 

When we talk about building AI agents, most of the focus is on infrastructure: how to connect agents to the right systems, how to manage access controls, and how to ensure reliable, secure data delivery. And yes, those are critical components—after all, agents can’t do much if they don’t have access to the right information at the right time. 

But there’s another challenge lurking beneath the surface, and it’s one that doesn’t get nearly enough attention: the human side of AI agent adoption. 

The Hidden Cost of Getting Comfortable with AI 

AI agents are fast, smart, and conversational. In fact, they’re often so good at mimicking human interaction that we can’t help but treat them like coworkers. And while that may sound a bit futuristic (or even creepy), it’s perfectly normal. Humans are wired to anthropomorphize. When something responds to us in real time, we naturally begin to build a sense of relationship. 

And that’s where things get tricky. 

Team dynamics research consistently shows that it takes several months for human teams to learn how to work effectively together. We spend that time learning to understand each other, figuring out communication styles, and avoiding the kinds of missteps that cause friction or offense. It’s part of why new team onboarding always comes with a ramp-up period. 

With AI agents, the process is functionally similar—except instead of two people adapting to each other, it’s a person learning to understand a tool that talks like a person but doesn’t think like one.  

Learning to Work with Agents 

Even when an AI agent is programmatically flawless, that doesn’t mean people will use it correctly—or trust its output. Proficiency with AI agents comes with practice. Just like a new coworker, agents need time to “settle in” as team members figure out how to interact with them, what they’re good at, and what their limitations are. 

This is more than just theory. Sam Altman, CEO of OpenAI, recently joked that the number of users saying “please” and “thank you” to ChatGPT was costing his company millions in compute time. Whether or not you’re polite to your AI agent, the sentiment reveals something important: we treat AI like a teammate long before we fully trust it to act like one. 

So, what’s the implication? The faster you get AI agents into people’s hands, the faster they’ll get comfortable. It’s one thing to know intellectually that we can’t offend an AI, that it’s okay to tell it that it’s wrong and it should try again; it’s another thing to believe it. And the sooner that happens, the sooner your organization can realize real business value from your investment in AI. 

Data Access is the Real Bottleneck 

Unfortunately, getting AI agents into production isn’t always fast or easy. 

Even the most advanced agents are limited by one critical constraint: data access. Most organizations have fragmented, siloed data spread across hundreds of apps and systems. Connecting an agent to that information in a way that’s secure, accurate, and up to date is no small feat. 

This is where most agent projects stall. Demos run great in controlled environments. But the moment the agent is supposed to go live, it runs into permissions issues, incomplete data sources, outdated pipelines, or brittle integrations. That’s the moment the excitement fizzles, the project gets shelved, and the opportunity to build proficiency and trust never materializes. Even worse is when the agent gets bad data and produces bizarre results. It doesn’t take more than a couple such breaches of trust to set adoption back by months. 

Accelerating Trust, Not Just Tech 

Enter SWIRL AI Search. SWIRL solves the data access problem at its root by acting as intelligent middleware between agents and enterprise data. Instead of requiring massive data migration or complex ETL workflows, SWIRL uses a Zero ETL architecture to search data in place—across 100+ enterprise applications. 

This means AI agents can access what they need securely, in real time, without duplicating data or creating new risk surfaces. Even better, SWIRL filters, ranks, and summarizes results using AI, ensuring that both humans and agents get actionable, trustworthy information—not just a pile of documents. 

But SWIRL’s greatest benefit may be this: it helps organizations move faster. By removing the friction from agent deployment, SWIRL allows teams to begin interacting with agents sooner, shortening the human trust curve and speeding up time-to-value. 

The Speed of Trust 

Agent adoption isn’t just a technical challenge—it’s a human one. The real transformation happens not just when the agent is “ready,” but when the people using it are. 

SWIRL helps you get there faster—by powering secure, real-time agentic search and reducing the barriers between your agents and your data. Because building trust with your AI coworkers doesn’t start when everything’s perfect—it starts the moment they show up. 

Ready to bring your agents to life? Download our white paper, try the open source version, or request a demo.