By Alastair Goldfisher
Veteran journalist and creator of The Venture Lens newsletter and The Venture Variety Show podcast. Alastair covers the intersection of AI, startups, and storytelling with over 30 years of experience reporting on venture capital and emerging technologies.

When the enterprise IT team at one of NeuBird’s clients started receiving detailed incident reports in their system, they assumed a new hire had joined.

The IT team began asking, “Who is this new employee? These reports are really good,” said Gou Rao, co-founder and CEO at NeuBird.

The client’s employees didn’t know the reports were being generated by Hawkeye, NeuBird’s AI agent.

For Rao, the confusion was a compliment, but it also points out a blind spot. What happens when software is so convincing, employees forget it isn’t human?

That’s the promise and the concern of agentic systems. AI tools are increasingly being deployed deep inside enterprise workflows, responding to alerts, solving infrastructure issues and doing it so stealthily that teams barely notice.

But that invisibility raises new questions: who’s in charge when the software acts independently? And what happens when something goes wrong?

Founded in 2023 and backed by Mayfield and Microsoft, NeuBird develops an agentic AI platform focused on IT operations. Their flagship tool, Hawkeye, connects with systems like PagerDuty or ServiceNow and monitors infrastructure for anomalies.

It pulls from logs, traces, alerts and telemetry to generate real-time diagnostics—what Rao calls “root cause analysis reports”—before a human looks at the issue.

The product aims to reduce outages, speed up recovery and cut downtime-related costs. Rao cited one study estimating $400 billion in global losses from unplanned IT outages in a single year. In NeuBird’s early deployments, he said, customers saw as much as an 80% reduction in time spent resolving incidents.

But unlike some AI tools that require teams to change how they work, NeuBird designed Hawkeye to operate in a system’s background.

“We don’t ask teams to retrain or adopt new workflows,” Rao told me. “The agent adapts to them.”

In practical terms, that means Hawkeye receives the same alerts as a human would, but instead of forwarding them, it begins investigating and offering answers.

That seamless integration, Rao said, is the only way to make agents viable in a high-stakes enterprise environment. But it also means the automation can go unnoticed and unexamined.

Rao is quick to point out that AI agents don’t magically “know” what matters.

“There’s too much data, too many logs, metrics, alerts. You can’t expect an LLM to reason effectively if you just dump raw telemetry into it,” he said.

His team’s solution is to treat agents like new employees. Provide them with good inputs, train them on company context, and allow for a learning curve.

“We tell customers Hawkeye will work well out of the box, but like any new engineer, it improves when you teach it your processes,” he said.

That comparison may be helpful, but it underscores a leadership reality: if agents require onboarding and oversight, then someone must be responsible for reviewing their actions and ensuring they’re aligned with company goals.

Hawkeye provides transparency by citing its sources and outlining its chain of thought, but Rao acknowledges that the need for some human validation never fully disappears.

In this way, agents don’t just reduce human labor—they’re reshaping it. Alert fatigue may drop, but someone still needs to supervise how the agent filters, clusters and responds to incidents.

Rao noted how much work is required for humans to manually sift through hundreds of daily alerts. Hawkeye may alleviate that burden, but it’s not a hands-free or fully autonomous system.

At the enterprise level, there’s growing interest in deploying agents across the org and not just in IT, but in sales, marketing and operations, Rao said. Unlike conventional software, agents can’t be judged by rigid outputs. They are, in his words, “more cognitive,” and their behavior may shift depending on the problem.

That makes managing them a new kind of leadership challenge.

Agents operate asynchronously. They reason. And while Rao believes they “make quality of life better” for teams, he also warns that they can’t be treated like plug-and-play tools.

In some ways, the success of Hawkeye may be its biggest risk: when automation fades into the background, it’s easy to stop paying attention.

“You have to ease it in,” he said. “You can’t ask people to change overnight, but you can’t just ignore the agent either.”

In the coming years, he expects enterprise AI agents to become commonplace, as they’re integrated into workflows, monitored like teammates, and, increasingly, personalized.

Rao pointed to one customer in Latin America whose engineers, after realizing Hawkeye wasn’t human, asked if it could deliver responses in Spanish and even suggested tweaking its tone.

“That kind of personalization,” he said, “is probably the next step for agentic systems.”

It’s a small but telling signal that the more AI agents resemble coworkers, the more teams expect them to behave—and sound—like one of their own.

The moment we start asking AI agents to sound like teammates, we risk treating them like teammates. But ask why an AI agent failed, and you may get a confident answer, not a clear one.

That’s why supervision, even if it’s ever so subtle and ongoing, matters more than ever.

Q: What inspired NeuBird to create Hawkeye, and what problem is it solving?
A: Rao explains that Hawkeye was built to tackle alert fatigue and infrastructure downtime in IT teams. By connecting to tools like PagerDuty and ServiceNow, the system monitors logs, traces, and telemetry to create detailed root cause analysis reports before a human intervenes.

Q: Why does Hawkeye’s invisibility present both a strength and a risk?
A: Rao says Hawkeye works best when it blends in. “We don’t ask teams to retrain or adopt new workflows,” he said. “The agent adapts to them.” However, he cautions that when automation is invisible, it can lead to unexamined decisions—and without oversight, errors can go unnoticed.

Q: How does NeuBird ensure its AI agents remain accountable?
A: Rao stresses transparency and traceability. Each diagnostic report cites its sources and outlines its chain of thought, ensuring human validation remains part of the process. “Like any new engineer, it improves when you teach it your processes,” he said.

Q: What leadership challenges come with deploying AI agents?
A: Rao notes that AI agents reason asynchronously and can’t be measured by static output. Leaders must manage uncertainty, treating agents as cognitive collaborators rather than tools. “You can’t ignore the agent either,” he said. “You have to ease it in.”

Q: What’s next for AI agents in enterprise environments?
A: Rao expects the future to include personalized agents integrated across business functions. One Latin American client asked Hawkeye to respond in Spanish and adjust its tone. Rao sees this as the next step—but warns it blurs lines between software and coworker, reinforcing the need for ongoing human supervision.

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Editor’s Note: This article was written by Alastair Goldfisher and originally appeared in The Venture Lens. Republished here with permission.