Humans Shift Above the Loop: Supervising Agents in the Agentic Organization

As organizations move into the “agentic” era, the role of people evolves very differently. Instead of spending time on every operational step, employees increasingly orchestrate outcomes—supervising AI agents, making trade-offs, setting goals. They step “above the loop,” observing workflows instead of executing them.

"Employees shift from performing tasks to orchestrating outcomes, supervising AI agents, setting goals, and managing trade-offs. Humans move 'above the loop,' overseeing workflows instead of completing every step."

— Brian Heger (summarizing McKinsey), Oct 2025

The Agentic Organization paradigm

McKinsey describes the agentic organization as one where AI agents—both virtual and physical—work alongside humans in a hybrid operating model. The workforce pillar is especially disrupted: humans are no longer just doing tasks; they become the stewards of workflows, the designers of abnormal use cases, and the human touchpoints in decision points. (Brian Heger - The Agentic Organization)

Emergence of new roles & responsibilities

In this new era, three profiles are gaining prominence:

• Supervisors: generalists fluent in AI, orchestrating multiple agentic systems.
• Specialists: experts who handle exceptions, redesign workflows, bring domain depth.
• AI-augmented frontline workers: roles where AI handles repetitive tasks, freeing people to focus on strategy and human connection.

Implications for HR, leadership, and culture

The rise of an agentic workforce changes foundational talent systems:

• Workforce planning now includes both AI agents and humans.
• Performance reviews shift from tasks completed to outcomes enabled.
• Learning & development expands from basic AI literacy to systems thinking, judgment, and ethical decision-making.

Culture becomes the glue: trust, clarity, ethical values, and purpose must tether high-speed AI change to human concerns. (The Agentic Organization: Contours of the next paradigm for the AI era)

Building agentic teams: structure and dynamics

Successful agentic organizations don't just add AI agents—they restructure around them. Key principles include:

Small, multidisciplinary squads: Cross-functional teams of 3-7 people, each owning end-to-end outcomes rather than narrow functions. These teams include AI specialists, domain experts, and business leaders.

Agent-first workflows: Workflows designed with AI agents as primary actors, with humans providing oversight, exception handling, and strategic direction.

Continuous learning loops: Systems that capture feedback from both human operators and AI agents, enabling continuous improvement of both processes and algorithms.

Skills transformation: from task execution to orchestration

The shift "above the loop" requires fundamental reskilling:

• Systems thinking: Understanding complex interactions between multiple AI agents and human operators
• Judgment and ethics: Making nuanced decisions about when AI recommendations should be overridden
• Data literacy: Interpreting AI outputs and understanding their confidence levels and limitations
• Creative problem-solving: Designing new workflows that leverage AI capabilities in novel ways

Leadership in the agentic era

Leaders must evolve from command-and-control to orchestration-and-governance models:

• Strategic orchestration: Designing the overall system of agents and humans working together
• Ethical governance: Establishing guardrails for AI deployment and usage
• Culture stewardship: Building trust and psychological safety in human-AI collaboration
• Innovation catalysis: Creating environments where experimentation with AI agents is encouraged

Real-world implementations

Leading organizations are already implementing these principles:

Tech companies: Software development teams using AI agents for code generation, testing, and deployment, with developers focusing on architecture and complex problem-solving.

Financial institutions: Trading operations where AI agents handle routine analysis and execution, with humans managing risk parameters and strategic decisions.

Healthcare systems: Diagnostic workflows augmented by AI agents for initial screening and data analysis, with physicians focusing on interpretation and patient care.

Challenges and solutions

Transitioning to agentic models isn't without hurdles:

Resistance to change: Address through clear communication about the value proposition and phased implementation.

Skills gaps: Invest in targeted training programs and bring in external expertise during transition periods.

Trust issues: Build gradually through small pilots with clear success metrics and transparent AI decision-making processes.

Integration complexity: Start with well-scoped use cases and expand systematically.

Conclusion

The agentic organization deepens the divide between doing tasks and directing outcomes. For employees, that puts judgment, design, and orchestration at the center. For leaders, it demands clarity of purpose, deliberate role design, and meaningful investment in both human and AI fluency. Organizations that master this transition will unlock unprecedented levels of productivity and innovation, creating a competitive advantage that traditional companies cannot easily replicate.

Sources

• Heger, B. (2025, October). The Agentic Organization: Contours of the next paradigm for the AI era. Read the article
• McKinsey & Company. (2025, September). The Agentic Organization: Contours of the next paradigm for the AI era. Read the full report