“We’re Already AI Managers,” Says Hebbia Founder. Read To Know How Hebbia Rewrites Work

The future of work has become more than humans using AI tools. It’s about AI agents becoming actual employees. While most companies treat artificial intelligence as sophisticated software, Hebbia has introduced something radically different and those are AI agents that function as genuine members of the workforce, complete with assigned roles, responsibilities, and even their own email addresses.

This isn’t theoretical. Today, Hebbia’s AI agent employees are actively working at nearly half of the world’s largest asset managers and top-tier law firms, fundamentally changing how these organizations operate.

Redefining the Employee

George Sivulka, Hebbia’s founder, describes a sea change already underway in organizational design. 

“Just as remote work decoupled the output of labor from location, with AI and these agent employees, you’re starting to see output completely decoupled not only from location but from whether or not you can pay salaries or have humans doing the jobs.”

The result is what Sivulka calls the emergence of three organizational types: fully human organizations, fully AI organizations (like the theoretical one-person billion-dollar startup), and most commonly, hybrid organizations where AI agents and human employees work side by side.

In this new paradigm, an AI agent employee is “another node in your org chart.” As he explains, “If that’s a node in your org chart, it will probably have to have an email, it’ll probably have to have a Slack, it’ll probably be doing things the wrong way and you’ll have to manage it in the right direction.”

How AI Agent Employees Actually Work

Unlike traditional AI chatbots that respond to single queries, Hebbia’s agent employees operate more like human knowledge workers. They can be assigned complex, multi-step projects that require processing vast amounts of information, making connections across different data sources, and producing comprehensive analytical work.

Through Hebbia’s Matrix platform which functions like a sophisticated project management interface, managers can assign multiple interconnected tasks to different AI agents simultaneously. Each cell in the grid becomes a discrete assignment completed by an agent employee, whether that’s analyzing SEC filings, conducting market research, or performing legal due diligence.

The scale is staggering. Hebbia’s agent employees collectively process 4-5 billion pages annually, compared to the 100 million pages processed by major consumer AI platforms. They operate through 250 billion large language model calls per month, managed by Hebbia’s “Maximizer” system that coordinates AI agent workloads like an air traffic controller.

The Manager-Agent Relationship

Perhaps most intriguingly, Hebbia’s approach is already transforming what it means to be a manager. “In a few years, we’re already AI managers,” Sivulka observes.

“The difference is that current AI takes a single step. As agents are rolled out, you’ll actually start to see people that are really good at prompting really good at defining a process be the best managers.”

This represents a fundamental shift in management skills. Traditional management focused on directing human employees through motivation, communication, and performance evaluation. Managing AI agent employees requires different competencies: precise process definition, effective prompting techniques, and the ability to architect complex workflows across multiple agents.

“Everyone will be prompting, and prompting is managing,” Sivulka predicts. “It will all blur pretty soon.”

How Wall Street Uses AI Employees

In asset management, AI agents simultaneously analyze hundreds of company filings to identify market inefficiencies, conduct comprehensive due diligence across multiple opportunities, monitor regulatory changes and assess portfolio impacts, and generate detailed research reports by synthesizing information from thousands of sources.

Law firms deploy AI agents for large-scale document review and analysis, legal research across vast case law databases, M&A due diligence, and regulatory compliance monitoring. These agents handle work that previously required teams of junior lawyers working for weeks or months.

The key breakthrough is persistence and contextual understanding. These AI agents can be redirected, learn from mistakes, and maintain institutional knowledge about ongoing projects just like human employees. This represents a fundamental shift from task-specific automation to AI systems that genuinely augment professional teams, potentially reshaping career structures as traditional entry-level analytical work becomes increasingly automated.

The Economics of Agent Employees

The financial implications are profound. Hebbia’s clients report that AI agent employees can replace expensive third-party services like  legal consultancies, expert networks, and specialized research firms, while providing capabilities that exceed human performance in data processing and analysis.

But the real value isn’t cost reduction it’s capability multiplication. “You can actually make way more money with AI,” explains Sivulka. “Having infinite expert employees” means organizations can pursue opportunities and conduct analysis at scales previously impossible.

Hebbia invests heavily in agent capability, spending up to $10,000 in model costs per user annually. “Intelligence will become too cheap to meter,” Sivulka predicts, justifying this investment in agent employee performance.

Managing 250 Billion AI Conversations

Operating AI agents at workforce scale requires entirely new infrastructure approaches. Hebbia’s technical architecture goes far beyond traditional AI implementations:

Their proprietary ISD (Inference, Search, Decomposition) system allows agent employees to recursively analyze documents, maintain context across complex projects, and synthesize insights from multiple sources. Unlike simple retrieval systems, this architecture enables true reasoning and connection-making across vast datasets.

The Maximizer system manages computational resources across hundreds of simultaneously operating agent employees, optimizing performance and cost while maintaining quality standards that exceed human-level accuracy.

What This Means for the Future of Work

Hebbia’s success suggests that it is witnessing the early stages of a fundamental transformation in how organizations operate. The companies that have quietly adopted AI agent employees nearly half of the world’s largest asset managers may be gaining competitive advantages that will prove decisive in coming years.

“We will still be deploying AI in its current form as a chatbot in 10 years,” Sivulka acknowledges, recognizing that organizational change takes time. “But everything is going to be backloaded; the change will happen in the decade.”

The implications extend beyond efficiency gains. In Sivulka’s vision, success in coming years will be defined by how effectively organizations can “put capable truly capable AI in the hands of as many people as possible, “not as tools, but as genuine AI agent employees that augment human capability at unprecedented scale.

For industries built on information processing and analysis, the question isn’t whether AI agent employees will become commonplace, it’s whether organizations can adapt their management practices, workflows, and competitive strategies fast enough to capitalize on this transformation.

As Sivulka puts it, the goal more than just building better software it’s about “building capable AI platform for a billion people” where AI agents function as genuine members of the workforce. Based on Hebbia’s rapid adoption among sophisticated financial and legal institutions, that future may be arriving faster than anyone expected.