What’s Stopping You? Overcoming the Three Most Common Obstacles to Building AI Agents

You’ve got the vision. The pilot runs beautifully. Everyone’s excited about automation, AI-powered productivity, and the promise of agents that can handle everything from summarizing meetings to drafting reports and answering questions. But just when it’s time to scale… progress stalls. Reality obnoxiously asserts itself. That promising AI agent project starts to fizzle, and no one’s quite sure why.

So what’s going on?

Welcome to the POC Valley of Death

According to BCG, 74% of organizations struggle to move AI projects out of the proof-of-concept (POC) phase, and only 4% have mature, cross-functional AI capabilities. Despite the growing hype and investment in AI agents, many companies hit the exact same wall at the exact same time: right between “this is amazing” and “why can’t we make this work?”

The problem isn’t ambition, it’s architecture.

Quick Demos Fall to Complex Reality

Building an AI agent in a sandbox is one thing. But putting that agent to work in a production environment—where it needs real-time data access, enterprise-grade security, and the ability to navigate silos and legacy systems—is something else entirely.

What seems simple in a test environment suddenly breaks down when:

The agent can’t access the right data, or any data at all.

Information is scattered across apps, departments, and disconnected systems.

Integrating new tools with existing security and infrastructure proves more difficult than expected.

The agent starts making mistakes—or worse, hallucinating—because it lacks the context or structure needed to reason effectively.

Data is the Fuel, Not the Feature

The biggest misconception about AI agent development is that the model is the hard part. In reality, the AI model is rarely the bottleneck. The real problem? Getting timely, secure, structured access to your data.

Enterprise environments are notoriously complex. Many companies operate hundreds or even thousands of applications, each with its own data format, access rules, and quirks. Add to that the ever-present security and compliance considerations, and suddenly your smart little AI agent can’t go anywhere.

Like a rocket, it looks impressive—but without fuel it’s not getting off the ground.

Enter SWIRL: Agentic Search That Scales

To escape the POC Valley of Death, you don’t need more AI—you need a better environment for AI agents. That’s where SWIRL AI Search comes in.

SWIRL acts as intelligent middleware between your agents and your data. It supports MCP, connects to over 100 enterprise systems—email, Slack, SharePoint, Tableau, CRMs, databases, and more—and gives agents real-time access to the information they need, all without requiring data migration or duplication.

Here’s how SWIRL helps you scale:

Zero ETL: No need to consolidate data into a central repository. SWIRL leaves data in place and queries it live.

Security-first: SWIRL integrates with your existing permissions so users and agents only see what they’re allowed to.

Agent-ready metadata: SWIRL extracts and organizes key metadata so agents can quickly find and use the right data.

Confidence-based ranking: Ensures agents base their responses on high-quality, relevant data—not just the first match.

From Fizzle to Flight

Once you remove the friction, your agents operate as intended—handling tasks, automating workflows, and helping your teams focus on strategic work instead of repetitive chores. Even better, they can be deployed faster, with fewer resources, and without reengineering your entire tech stack.

The result? A successful launch from pilot to production.

Escape Velocity

So—what’s stopping you from building AI agents?

Complexity? SWIRL simplifies.

Security? SWIRL protects.

Data access? SWIRL delivers.

Ready to escape the POC Valley of Death and take your agents live? Download our white paper or contact us for a demo.