Reframing Processes with AI Agents
An Understanding AI agents and their role in business transformation is becoming critical in today’s digital landscape. This module introduces the evolving concept of AI agency, guiding participants from current, narrow-function systems to more advanced agents that approximate human-like adaptability and decision-making.
Participants will explore the five key dimensions of AI agency—adaptability, proactiveness, complexity of goals, complexity of environment, and autonomy. These concepts provide a framework to evaluate and design intelligent systems that can act with increasing levels of independence and contextual awareness.
By the end of the module, learners will have a strategic understanding of how AI agents can redefine workflows, enhance automation, and reshape business processes, along with insight into the risks, limitations, and integration challenges involved in real-world deployment.
AI Agents and Agency
Acknowledge what AI agents are, how they differ from traditional automation systems, and why they matter for digital transformation.
Five Dimensions of Agency
Identify and analyze the five key attributes of AI agency—adaptability, proactiveness, goal complexity, environmental complexity, and autonomy—and evaluate their relevance in real-world scenarios.
Stateful/Stateless Agents
Recognize the difference between stateful and stateless AI systems, and understand the implications of context retention for system design and performance.
AI-Enabled Workflows
Reinterpret existing processes using AI agents by designing conceptual blueprints or service workflows. Define the agent’s purpose, required data sources, operational environment, and expected outputs within the context of broader system change.
Governance Models
Design AI agents whose goals align with organizational values, user needs, and long-term impact. Understand how to frame agent goals, define control mechanisms, and incorporate lifecycle governance (updates, oversight, auditing).
Risks & Challenges
Critically evaluate the risks, limitations, and ethical implications of implementing AI agents in human systems. Consider issues such as trust, control, system interoperability, accountability, and unintended consequences.
Target
This module is designed for mid- to advanced-level professionals and decision-makers across a wide range of fields, including but not limited to: Designers and engineers, Policymakers and legal experts, Researchers and academics, Consultants, strategists, and innovation leads, Educators and facilitators, Activists and community organizers.
A working familiarity with AI tools or automation platforms is recommended, as the course builds upon foundational concepts to explore advanced use cases and agent design practices.
Whether deploying AI agents in enterprise operations, automating service flows, or rethinking how digital tools interact with human systems, this module provides a strategic and practical entry point into a fast-evolving field.
Acquired Competences
Design-Oriented Understanding of AI Agency
Develop a strategic understanding of what makes an AI agent different from traditional automation systems. Apply the five dimensions of agency—adaptability, proactiveness, goal complexity, environmental complexity, and autonomy—to analyze existing tools and design agent-based systems that respond to real-world contexts. Assess where and how agency can add value across business, public, and creative domains.
Context-Aware System Modeling
Map and redesign existing workflows by integrating AI agent logic. Distinguish between stateful and stateless systems to determine how memory, environmental complexity, and contextual awareness influence the agent’s design. Define the agent’s function, required data, and intended outputs, and express this through conceptual blueprints or service process models that align with broader system goals.
Design for Value Alignment and Lifecycle Governance
Frame AI agents around values that reflect organizational, ethical, and human-centered priorities. Establish mechanisms for long-term governance—such as oversight, updating, and evaluation—to ensure agents remain accountable, relevant, and aligned with evolving needs.
Critical Risk and Integration Assessment
Evaluate the risks and limitations of integrating AI agents into human systems, including issues of trust, interoperability, power asymmetry, and unintended consequences. Recommend strategies for responsible implementation, continuous supervision, and ethical alignment in hybrid ecosystems.
This course is especially valuable for professionals rethinking how work, services, and systems can evolve through AI agency—whether in business, public sector innovation, education, or creative industries.
