In many organizations, AI agents are currently being built. They answer questions, automate processes, or support information retrieval. While agents can efficiently handle individual tasks, they often lack integration into more complex organizational contexts.
The transition from agents to true virtual employees is therefore a decisive step — and it requires a fundamental shift in how organizations approach artificial intelligence.
What exactly are AI agents — and what are they still missing?
AI agents are often described as autonomous software entities that pursue specific goals and perform actions based on rules, models, or data (IBM, 2024).
In practice, agents are typically designed to handle clearly defined tasks or sub-steps within predefined workflows — for example in automation, information processing, or standardized interactions.
They are highly specialized but generally not designed to work flexibly and sustainably as integrated members of teams and organizational processes.
In addition, agents today are usually deployed in a highly deterministic, rule-based manner. They act reactively, within strict system boundaries, and often in relatively simple environments.
As a result, the true potential of modern AI often remains untapped. Agents are extremely useful tools for well-defined tasks, but they are limited in their role and scope of action.
The Agency Gap: Why agents reach their limits
The so-called AI Agency Gap describes precisely this discrepancy. Gartner (2024) uses the term to describe the gap between what today’s AI systems and agents can achieve and the level of agency demonstrated by human employees.
While fully deterministic chatbots are limited to simple, reactive tasks in stable environments, modern LLM-based assistants and agents already offer greater flexibility — without coming close to human-level agency. The AI Agency Gap becomes particularly visible when autonomy, proactive behavior, and the handling of complex tasks in dynamic environments are required.
Artificial Experts: The next generation of virtual employees
Artificial Experts bridge this gap. They represent an advanced approach to the productive use of AI in organizations. Unlike classic agents, they are not designed as tools, but as virtual employees.
They are characterized by:
- a persistent role and identity
- a stable, long-term memory
- competencies and skills
- multimodal communication capabilities across channels
- the ability to collaborate with humans, systems, and other AEs
- the capacity to learn from experience and continuously evolve
Through AI-based training paths and their ability to learn, Artificial Experts are purposefully trained and become operational very quickly. Instead of executing isolated tasks, they take over complex areas of responsibility and act proactively — unlocking the full potential of modern AI.
Training as the key: From tool to workforce
A central difference between classic agents and Artificial Experts lies in the quality and structure of their training.
Artificial Experts are:
- trained through structured training paths
- supported by AI-based trainers (such as Emma)
- continuously developed within a dedicated virtual coworking space
The evolution from AI agents to Artificial Experts marks a fundamental paradigm shift in how organizations work with AI systems.
Agents are tools that efficiently execute tasks — largely deterministic and limited to specific workflows. Artificial Experts, by contrast, are virtual employees who take on roles and responsibility areas and grow with their challenges.
This enables a new form of collaboration: AI is no longer just a technical tool in the background — it becomes a true virtual workforce.