Unlocking the potential of the human-agent hybrid workforce 

The workplace has entered a new world. AI agents are no longer experiments at the edges of operations — they’re answering questions, moving routine tasks forward, and freeing people to focus on higher-value work. This is human-agent teaming in action. But technology alone won’t get us there. The real unlock is rethinking how work is viewed and managed, so humans know exactly how to lead and partner with AI agents for the highest impact.

Job architecture has long served as the backbone for organizational clarity, talent management, and workforce planning. It defines roles, responsibilities, career paths, and compensation frameworks.

Historically, job architecture has been designed around human-to-human workflows and capabilities.  But now, with AI agents embedded into everyday work, those structures need to evolve. Mercer’s latest Global Talent Trends research shows that leading organizations are reimagining roles and harnessing augmented intelligence, embracing human-agent hybrid workforces, where humans and machines collaborate seamlessly.

This trend is not merely about automation replacing jobs but about reimagining roles to harness augmented intelligence. Humans are managing, directing, and collaborating with AI agents that amplify productivity and decision-making. But without updating job architecture to reflect these dynamics, organizations risk misaligned roles, unclear accountabilities, and undervalued technology investments.

Microsoft’s 2025 Work Trend Index Annual Report reinforces this point: the most successful “frontier firms” blend human creativity with advanced technology, which requires a fundamentally new approach to job design and workforce management.

In this new model, humans are no longer working in isolation or alongside other humans. They are managing, directing, and collaborating with AI agents that augment their productivity and decision-making. This hybrid human-agent workforce introduces new dynamics that traditional job architectures do not adequately capture.

When thinking about how job architectures will need to evolve, we see this take place across five key dimensions. 

Redefinition of role boundaries and accountabilities

Start by updating role definitions to include clear responsibilities for managing AI agents. Clearly define who oversees agent performance, who checks outputs for quality and compliance, and who steps in when exceptions occur. For example, a customer service manager’s job description might now include oversight of AI chatbots handling routine inquiries, with clear escalation protocols for complex issues.

Mercer’s Talent Trends research shows that organizations leading in AI adoption are those that embed this kind of clear governance and accountability structures around digital tools, ensuring humans remain in control. This clarity helps maintain trust and operational excellence.

Requiring digital fluency as a core competency

Managing AI agents requires new skills. Roles should explicitly include skill requirements such as digital fluency, data literacy, and an aptitude for interpreting AI outputs. Integrating these into job profiles and career paths ensures talent development programs prepare employees not just to use technology, but to effectively govern and optimize it.

Indeed the Microsoft Work Trend Index report highlights digital dexterity as a key differentiator for high-performing organizations, making it essential to align workforce skills with business strategy.

Designing for human-agent hybrid work models

Human-agent teaming calls for hybrid work models that blend cognitive, emotional, and technical capabilities. Job families and levels should reflect this new category of job design, recognizing roles that combine traditional human skills with agent management. That can open the door to new job categories—such as “Agent Supervisor” or “Customer Success Orchestration Manager”—that bridge operational and technological domains.

Mercer’s research also points to the rise of “hybrid jobs” that combine human judgment with machine execution as a defining trend. Organizations proactively designing for the human-agent hybrid workforce gain agility and deepen employee engagement.

Evolving performance management and rewards

Performance metrics and incentives must evolve to capture the effectiveness of human-agent teaming. Traditional productivity measures alone won’t suffice. Instead, organizations should measure how well employees leverage agents to improve outcomes, say through better ideas, reduced errors, or enhanced customer experience. Compensation models should reward these new dimensions of value creation.

The Talent Trends report underscores the importance of aligning rewards with behaviors that drive innovation and collaboration with technology, ensuring motivation keeps pace with evolving job realities.

Strengthening governance and ethical oversight

As AI agents take on decisions that affect customers and employees, governance and ethical oversight must be built into job roles. Responsibilities should include ensuring that AI agent decisions align with organizational values, meet regulatory standards, and maintain fairness. Embedding these accountabilities within job architecture reinforces organizational trust and social license to operate.

Microsoft’s Frontier Firm research affirms that trust and ethical AI use are core to sustaining competitive advantage. Job architectures that incorporate these dimensions help embed responsible AI governance at the operational level.

Technology may be a catalyst for these changes, but the evolution of job architecture must remain firmly rooted in the idea of work and workers. The goal is not to replace people but to empower them to work smarter with AI agents. That means shifting the mindset from seeing AI agents as tools to recognizing them as collaborators that amplify human judgment, creativity, and impact. Functions across the organization need to adapt accordingly.

Job architecture is a powerful lever to embed this mindset at scale. By explicitly defining how humans interact with and manage agents, organizations create clarity, reduce ambiguity, and foster a culture of continuous learning and adaptation.

To evolve a job architecture effectively, organizations should:

1. Map Current roles to AI agent interaction points: Analyze existing roles and workflows to identify exactly where AI agents are deployed within the organization. Document how human employees currently interact with these agents, noting responsibilities such as oversight, exception handling, and decision-making. This mapping will help uncover gaps where job descriptions lack clarity around agent management and highlight opportunities to redesign roles for more effective human-agent collaboration.
2. Engage cross-functional stakeholders: Build a working group with HR, technology, operations, compliance, and line-of-business leaders to ensure that job architecture changes reflect both technological capabilities and operational realities while adhering to regulatory requirements. Engage stakeholders early and continuously to co-create integrated role profiles that balance human skills with agent management responsibilities, fostering buy-in and alignment across the organization.
3. Pilot new job models: Select specific business units or teams to trial updated job definitions and career paths that incorporate human-agent hybrid responsibilities. Use these pilots to gather qualitative and quantitative feedback on role clarity, skill requirements, and performance outcomes. Based on pilot results, refine job models iteratively before broader rollout, ensuring the new roles are practical, well-understood, and effectively support the hybrid workforce.
4. Update talent management systems: Review and revise talent management platforms to embed new competencies related to digital fluency, data literacy, and ethical oversight of agents. Update performance management frameworks to include metrics that assess how effectively employees collaborate with and manage AI agents. Adjust reward and recognition systems to incentivize behaviors that drive successful human-agent partnerships and continuous learning.
5. Communicate transparently: Develop a clear communication plan that explains the rationale behind job architecture changes, emphasizing how these updates enhance employee roles and career development opportunities. Use multiple channels, such as town halls, intranet, manager briefings—to ensure consistent messaging and address employee questions or concerns. Highlight success stories from pilot programs and provide resources for employees to build the new skills needed in the human-agent hybrid workplace.

The future of work will be defined by increasingly sophisticated AI agents working alongside people. Job architecture must move from a static framework to a dynamic enabler of this human-agent hybrid workforce. Organizations that proactively redesign their job architectures to reflect human-agent collaboration will unlock greater agility, innovation, and employee engagement.

In this future, the human element is paramount. Managing AI agents is not just a technical skill but a leadership imperative that demands empathy, judgment, and ethical stewardship. Organizations that embed these dimensions into job architecture, will be ready to thrive in the next era of AI in the workplace—one where human-agent teaming is a proven driver of sustained competitive advantage.