AI Agents and Human AI Co-Work touches on two interrelated topics: AI agents and their interaction with humans. It builds upon a basic understanding of Agentic AI and deepens it by elaborating on agent specific challenges such as continual learning, multi-agent collaboration, long running autonomous tasks, and tool usage. It also looks more holistically on how to manage a fleet of agents taking into account technical and human realities. This includes how to design human-AI interaction and workflows with AI given the changing roles of humans in the workforce due to AI agents. Roles of humans might increasingly shift towards managers, potentially defining their own agents or at least providing high level instructions and goals, monitoring agent execution, and reviewing outcomes. This requires mutual understanding. AI agents might take personalisation to the next level, as AI agents are increasingly integrated into our lives.
It also discusses research frontiers.

• Understanding and controlling AI: AI and human capabilities, explainability, interventions with causal AI, guardrails
• Structuring of workflows and jobs: Task decomposition, Modes of interaction (audio, text, visual), collaboration of humans and AI, humans as AI agent managers
• Agent system design: Tool usage, multi-agent systems, continual learning, memory, computational efficiency
• Evaluation of agents and human-AI workflows
• Governance, Safety, and Alignment

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• Real-life examples are used to show how the course content can be applied in practice.

After successful completion of the course, students will

Professional competence

• be able to create visualisations that inform business decision making
• recognise the typical challenges of visualising large and complex data sets

Methodological competence

• understand the main concepts, theories, and methods of data visualisation
• be able to use data-visualisation methods to analyse business problems, generate possible solutions, and compare these solutions in terms of their effectiveness and efficiency

Social competence

• discuss challenges and benefits of statistical graphics
• help others in group work

Personal competence

• identify new challenges and independently develop viable solutions
• reflect on their own and others’ visualisations

Technological competence

• be able to create graphs like bar charts, scatterplots, line charts, and heatmaps in R to represent various types of data sets visually
• be able to collect and prepare data before it can be visualised