From: aidotengineer
Hai, a company founded by Steven Moon, is focused on building Enterprise AI agents that operate within existing security boundaries, leveraging decades of enterprise infrastructure rather than creating parallel systems [00:00:04]. The primary challenge in deploying AI agents is ensuring these “digital workers” respect enterprise security, compliance, and established workflows [00:00:30].
A New Computing Paradigm
As AI Engineers, we are witnessing a unique moment where software applications can understand humans directly and use the same interfaces as people do [00:00:48]. Large Language Models (LLMs) represent a new computing paradigm, enabling AI agents to reason requests, understand context, and interact naturally through existing channels [00:00:56].
The Pitfall of Parallel Systems
Despite this potential, the current trend in deploying AI agents in enterprises often falls back into old patterns [00:01:09]. Each new AI agent frequently becomes another external system, requiring new portals, credentials, and security reviews [00:01:11]. Instead of embracing the new paradigm where agents interact like humans, many approaches are inadvertently building more barriers between users and new capabilities [00:01:21].
Sacha Nadella, CEO of Microsoft, observed a “fundamental shift” in business software, suggesting that “SaaS is dead,” implying that AI agents will become the primary way users interact with business systems [00:01:34]. Yet, many organizations are still building new AI agent portals and dashboards, recreating an obsolete pattern [00:01:53].
Enterprise AI Agents as Employees
Ideally, enterprise AI agents should function like any other employee [00:02:00]. This means:
- Following security policies [00:02:02]
- Using approved systems [00:02:05]
- Staying within data boundaries [00:02:07]
- Accessing only necessary information [00:02:09]
- Being monitored and audited like human staff [00:02:10]
The good news is that enterprises already possess the necessary infrastructure: secure compute environments, identity management, data governance, compliance frameworks, and audit capabilities [00:02:15]. These systems, refined over decades, allow for the execution of AI agents within existing private clouds and security boundaries [00:02:26]. Modern AI infrastructure supports running agents in private clouds, keeping data within tenants, utilizing existing security controls, leveraging current workflows, and maintaining complete oversight [00:02:38]. The technology is available to deploy AI with the same privacy controls applied to human employees [00:02:49].
“Every time we reflexively create a new interface for AI agents, we’re potentially solving yesterday’s problem. We’re building translation layers between humans and machines at exactly the moment when machines can finally understand us directly.” [00:02:54]
Before creating new portals or dashboards, the question should be whether the new capability can be delivered through systems users already know and trust [00:03:09].
Leveraging Existing Enterprise Infrastructure
AI engineers often overlook the power of existing enterprise infrastructure [00:03:21]. Platforms like Microsoft 365 are battle-tested and integrated into enterprises’ security and compliance frameworks [00:03:28]. By building effective AI agents on these platforms, they inherit the trust and infrastructure [00:03:40].
The IT Department as HR for AI Agents
Jensen Wong’s observation that “the IT department of every company is going to be the HR department of AI agents in the future” perfectly captures this transformation [00:03:55]. IT teams can provision AI agent accounts using existing Active Directory tools, apply standard security policies, set permissions via familiar interfaces, and use current audit and monitoring tools [00:04:09]. This means no new systems to learn; AI agents are managed like any other employee [00:04:24]. The IT department thus becomes the HR department for the AI workforce, managing onboarding, access, commissions, and monitoring through familiar systems [00:04:31].
Agent-to-Agent Communication
Email provides a powerful pattern for agent-to-agent communications [00:04:42]. Just as humans use email for collaboration, AI agents can email each other to share data and coordinate work [00:04:47]. This ensures:
- Every interaction is fully logged and auditable [00:04:57].
- Permissions are automatically enforced through existing systems [00:05:00].
- Data flows are transparent and controllable [00:05:03].
This framework allows for building effective AI agents that are observable and controllable at enterprise scale [00:05:06]. While Microsoft’s ecosystem is one example, these patterns are applicable to Google Workspace or other enterprise platforms [00:05:13].
Conclusion
The key insight for AI Engineers is the ability to leverage existing enterprise infrastructure rather than building parallel systems [00:05:20]. These platforms offer built-in identity management, established security controls, proven compliance frameworks, and enterprise-grade APIs [00:05:30]. This approach allows engineers to focus on building new capabilities and solving new problems, instead of reinventing infrastructure that already functions [00:05:38].
The future of Enterprise AI is not about building new interfaces for agents, but about enhancing existing systems [00:05:47]. By choosing universal and trusted methods like email, or by integrating with existing document management systems, internal messaging platforms, and workflow tools, organizations can rethink AI agent integration [00:05:55].
Since software agents can now directly understand human intent, each existing system becomes a potential gateway for AI capabilities [00:06:14]. This represents a fundamental shift in how enterprises approach AI adoption and application development [00:06:23]. Instead of asking what new tools are needed, the focus shifts to which existing systems can be enhanced with AI agents [00:06:31]. The most powerful solutions may not be new interfaces, but rather the “quiet intelligence” added to tools customers already trust and use daily [00:06:39].
The era of mandatory translation layers between humans and machines is ending, giving way to direct understanding and seamless AI collaboration [00:06:51].