Guy Meyer loves a challenge. “Once I start pulling on a thread, I have a hard time letting go,” he says. And in his eight years at ServiceNow, he’s seen his fair share of tough problems.
The latest challenge Guy, a senior staff product designer, is tackling: how best to keep track of the swarms of AI agents organizations are deploying to automate tasks across the enterprise.
Adoption of agentic AI, a type of artificial intelligence that acts autonomously to complete a specific goal, is surging. About 40% of executives are considering adding agentic AI tools to their tech stack within the next year, according to the ServiceNow Enterprise AI Maturity Index.
That’s great news for business leaders, who can scale projects more quickly, and for employees, who can tap AI agents for help with mundane tasks. But there’s a problem, one a designer is better positioned than anyone to solve. Companies bringing a lot of AI agents online rapidly need to be able to see what they’re doing.
"With AI agents, I wanted customers to have an immediate, high-level understanding of their AI landscape,” Guy says.
To provide it, he had to think outside the box. The right solution had to be functional, elegant and, most importantly, flexible enough to grow as more and more AI agents come online.
Enter the Hive Map, a real-time snapshot of agent activity across the enterprise—courtesy of Guy Meyer, AI cartographer.
Today, an organization might have just a handful of AI agents running projects across the entire enterprise, Guy explains. But as AI agents’ value becomes clearer, businesses will add hundreds, if not thousands, more.
Onboarding more AI agents can add powerful benefits to the business, empowering teams in areas such as IT and HR to do their work faster and more efficiently. However, the more AI agents there are on the platform, the harder it is for companies to visualize, track, and resolve issues.
“Lists, forms, and manual inspections don’t allow people to understand what’s happening or what’s changed,” says Guy. With thousands of agents on board, teams can easily overlook small changes in agents’ health or behavior that can have a tremendous impact down the line.
Explicability is a growing challenge for organizations deploying AI agents. Unlike traditional software, where it’s possible to trace every line of code, AI agents operate with a degree of autonomy that can feel opaque. They make decisions, trigger actions, and interact with systems—sometimes in ways the humans who manage them don’t fully anticipate.
This opacity creates real business risk—and a governance challenge. When an AI agent fails or behaves unexpectedly, teams need to understand not just what went wrong, but why:
- Which data sources was it pulling from?
- What other agents was it communicating with?
- What chain of decisions led to the failure?
Without clear answers to these questions, troubleshooting becomes guesswork, and trust in the technology erodes.
The challenge intensifies at scale. A single malfunctioning AI agent might be easy to diagnose. But when you're managing hundreds of agents with complex interdependencies, the lack of visibility becomes paralyzing. Companies can’t manage what they can’t see, and they can’t trust what they can’t explain.
Guy's solution addresses these challenges head-on. The Hive Map provides a visual interface that shows how everything is performing across the entire AI agent ecosystem.
Instead of managing agents one by one, users can filter by health, ownership, and connection. This gives teams a comprehensive view of their AI landscape: who owns each agent, whether it’s functioning properly, and what overall value it’s delivering to the organization.
The result is fewer blind spots and faster decision-making. Teams can quickly identify issues, understand dependencies, and take action before small problems become major disruptions. With the health, ownership, and value of agents visible at a glance, there's no need to hunt for information or scramble to address an issue only after it's turned into a crisis.
Now, customers are using the Hive Map to track the health of their agents in real time. More importantly, they're using it to confidently onboard new agents without worrying that they won't scale. When you can see the entire ecosystem and understand how new agents will fit into it, growth becomes manageable rather than chaotic.
For Guy, the Hive Map captures everything he loves about design. “Great design is about transforming complexity into clarity, reducing cognitive load, revealing meaningful structure, and enabling confident action at scale,” he says.
With technology becoming more complex, design will have to get much simpler. And as AI agents multiply across the enterprise, that transformation might be the difference between scaling up and melting down.
Find out how ServiceNow can help you put AI agents to work for people.
