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The ROI of Employee Experience
In offices around the world, a new coworker is showing up. It doesn't attend meetings, ask for leave or draw a salary — but it reads email messages, schedules calls, drafts reports, monitors systems and follows up with clients. Built on autonomous AI frameworks such as OpenClaw, these agents are increasingly part of everyday workplace workflows, running in the background with little human supervision.
Unlike traditional software, OpenClaw agents don't just execute single commands. They are given goals, make decisions along the way and act across multiple tools and platforms. Supporters say this marks a leap in productivity, freeing workers from repetitive tasks. Critics warn it blurs lines of responsibility and accountability.
As these systems move from experimentation to routine use, the central question is no longer whether they work. The question is how their presence is reshaping power, labor and responsibility inside the workplace.
A Peek at the Future of AI Agents
OpenClaw, an open-source autonomous AI assistant released in late 2025, gained more than 100,000 GitHub stars by promising to handle complex digital tasks with little supervision. What distinguishes these systems from traditional AI tools, according to Mitul Chittoory, who works on large-scale internal platforms at Microsoft, is their transformation from passive consultants to proactive operators capable of monitoring inboxes, identifying urgent requests, drafting responses and scheduling meetings autonomously.
The shift is more fundamental than efficiency gains suggest. These AI agents represent a change from software that waits for instructions to systems that pursue outcomes independently. The real transformation isn't speed but visibility, according to Lolita Trachtengerts at Spotlight.ai. These systems show how work actually happens rather than how organizational charts suggest it should happen.
The Unexpected Side Effect of AI Agents: Workplace Surveillance
Perhaps the most corrosive workplace effect isn't what autonomous agents do, but what they observe. These systems log every micro-action to learn and improve, creating perfect, granular records of workers' digital lives.
The difference between traditional monitoring and agent logging isn't just scale but kind. Traditional systems capture outcomes. Agent logging captures process: every hesitation, every alternative considered, every moment of human judgment that might deviate from the optimal path.
Consequently, organizations risk moving from measuring results to measuring every keystroke, Chittoory warned. "This level of data persistence can lead to a culture of performance theater where workers are afraid to deviate from optimal patterns," he said.
The surveillance is unintentional but real. AI agents log everything because they need evidence to function. "If companies don't set clear boundaries, agents will expose more than leadership is ready to confront," Trachtengerts said.
Alex Bovee, co-founder and chief executive at ConductorOne, an AI-native identity security platform, dismisses these concerns as overblown. Most security-centric organizations already have visibility and tooling in place to prevent data exfiltration and monitor where necessary, he said.
But this misses the point. The logging these agents perform doesn't just monitor what workers do, but also records how they think, what alternatives they considered, where they hesitated. Organizations unprepared to confront this reality will find themselves with surveillance infrastructure they never intended to build.
Chittoory advocates for a digital identity mandate requiring organizations to disclose when someone is interacting with an agent. "Trust is destroyed the moment someone realizes they've shared an emotional or complex concern with a machine thinking it was a human," he said. Trachtengerts frames it more cynically: "Transparency is cheaper than damage control."
Who's Responsible When AI Agents Fail
What's happening in most organizations bears little resemblance to the orderly deployment scenarios vendors describe. AI transformation today is largely employee-led, with shadow AI proliferating across teams. "Employees aren't just bringing their own tools; they're bringing their own workforce," Bovee said.
Unlike past shadow IT deployments that stored data, these systems take action. Organizations remain stuck on basics such as centralized visibility, clear ownership and managing access lifecycles. As adoption accelerates, the AI governance gap widens.
When an autonomous agent mishandles a client request, sends incorrect pricing or misses a compliance requirement, it isn’t clear who’s responsible. Chittoory argued that the person who delegated the task remains accountable for the final output. "Organizations should treat AI agents like interns, where they surely possess great potential but also make it necessary for rigorous oversight," he said.
But this assumes the agent-employee relationship mirrors traditional workplace hierarchies. "When we need accountability for an action, that should require human judgement. Otherwise, don't delegate it to the agent," Bovee said. The issue isn't who's accountable when an agent makes a mistake but why the agent was permitted to take actions requiring proper human oversight in the first place.
Accountability hasn't disappeared, but rather redistributed itself across organizational layers, Trachtengerts said. The individual deploying the agent owns configuration decisions. Management owns the guardrails and approval logic. The company owns outcomes. "If an organization cannot explain why an agent acted, that's a governance failure, not a technology failure," she said.
The problem is that these disagreements reveal how little consensus exists even among those building these systems, and organizations are deploying them anyway.
When AI Support Becomes Job Replacement
The boundary between AI agents acting as support for workers or a replacement proves even harder to define. Chittoory draws the line at reasoning vs. execution. It’s replacement when reasoning is outsourced, rather than just the tasks. If an employee is no longer required to understand the rationale behind a decision but only to approve it, their role has been automated.
"The line is crossed when judgment is removed, not when tasks are automated," Trachtengerts said. When an agent prepares, monitors and recommends while humans decide, that's augmentation. When it executes decisions affecting revenue, customers or compliance without human checkpoints, that's role replacement. But most companies aren't replacing people but rather exposing that work was never being managed in the first place, Trachtengerts said.
Bovee frames the transformation as evolution rather than elimination, arguing that AI is shifting knowledge workers to focus on defining what agents should do and validating their outputs. But this raises the question of whether that translates to job security or makes the humans themselves easier to replace.
Autonomy boundaries prove especially contested around external-facing work. The risk becomes more acute when agents gain unrestricted access to external communications without human gatekeepers, Chittoory said. "Once an AI can independently commit a company's resources or reputation, the potential risks far outweigh the benefits," he said.
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Focus should shift from autonomy itself to access and privilege, Bovee said. Most agents today are over-privileged by default, trusted with more authority than they should have. "Control hasn't disappeared, it's just moved up a layer from supervising every individual action to governing how agents are connected, what credentials they hold, and under what conditions they can act," he said.
The shift sounds reasonable until you consider how few organizations have the infrastructure to govern at that layer, or the incentive to build it when the current free-for-all is delivering productivity gains.
How AI Agents Widen Workplace Inequality
The most consequential question may be who gets left behind, and the answer is becoming more clear. Workers at top-tier firms with access to customized frameworks such as OpenClaw will gain tenfold productivity advantages over those at smaller firms. Employees who managerially lead AI outpace those who only use it. "Without universal upskilling, we are creating a two-tier workforce: those who manage the agents, and those who are managed by them," Chittoory said.
That inequality is already here, Trachtengerts contended. "Teams that know how to deploy and question agents will move faster and look smarter. Others will fall behind, not due to talent, but due to access and literacy," she said. The next workplace divide won't be AI vs. humans but humans who direct AI vs. humans who can't.
Skills required to manage these systems shift faster than training programs adapt. Systems orchestration — knowing how to string multiple AI agents together and audit their logic – is critical, Chittoory said. Trachtengerts describes the new skillset as learning how to frame intent, interpret evidence and challenge machine conclusions. Managing AI resembles managing analysts more than operating software.
Yet companies are investing in tools while lagging on comprehensive curriculum. "We can't give individuals Ferraris without teaching them how to drive," Chittoory said. "Very few companies are training for this. They buy tools. They skip education. That gap will become visible fast," Trachtengerts agreed.
The pattern is familiar. Technology deployment races ahead of workforce preparation, with predictable consequences for those without access to premium training and tools. What emerges is not the runaway AI narrative that dominates headlines but something potentially more troubling: a transformation of workplace power structures happening faster than governance keeps pace, with benefits accruing disproportionately to those already ahead.
Whether human workers will have any meaningful say in how autonomous systems reshape their work remains an open question.
Editor's Note: What else is happening in the world of AI agents?
- 2025 Was Supposed to be the Year of the AI Agent. It Never Arrived — Sam Altman proclaimed 2025 the year of the agent. In 2026, most still fail real work — but signs of the future are emerging.
- Moltbook's AI Agent Internet Falls Apart Over Simple Security Flaw — Moltbook's database breach exposed more than API keys — it showed how unprepared companies are to secure, govern and prove accountability for autonomous agents.
- Why AI's Economic Promise Depends on What We Build Around It — Technology reliably creates wealth, but it does not reliably create welfare. This explains why AI feels both exhilarating and destabilizing.