Terms from the field.

The effort a person or organization must invest to use a new tool productively. The higher the barrier, the slower the adoption.

An AI agent is an artificial intelligence system that autonomously executes multi-step tasks – not just providing single answers, but planning, acting, evaluating results, and iterating. Unlike a chatbot, an agent can use tools, write and execute code, edit files, and interact with external systems.

Research field addressing moral questions of AI system deployment. Encompasses technical approaches (algorithmic fairness, explainability), regulatory frameworks (EU AI Act), and philosophical groundwork. Internally heterogeneous, no unified methodological approach.

The process of ensuring AI systems act in accordance with human values, intentions, and safety requirements. Goal: the system reliably does what humans want – even in unforeseen situations.

Hypothetical design pattern: AI systems that work in the background (updating context, checking consistency, preparing suggestions) and only move to the center of attention when something relevant occurs. A counter-proposal to the dominant chat paradigm.

Design philosophy formulated in 1995 by Mark Weiser and John Seely Brown (Xerox PARC). Core idea: The best technologies require minimal attention and move fluidly between the periphery and center of perception.

Jürgen Habermas’s term (1981) for mutual understanding under conditions that enable equal participation for all involved: comprehensibility, the possibility of objection, no structural asymmetry.

The computational capacity required to train and run AI models. Typically measured in GPU-hours. Concentration of compute among a few actors is a central geopolitical issue.

Context Engineering refers to the ability to provide an AI agent with the right context – all the information, rules, and constraints it needs to solve a task well. The better the context, the better the result. In agent-driven workflows, this competency increasingly replaces manual coding as the engineer’s central skill.

Continuous Deployment means that code changes are automatically delivered to the production environment without a manual release step – as soon as all automated tests pass. Combined with feature flags and observability, it enables fast iteration cycles with controllable risk.

The design of data visualizations and data-driven interfaces – from charts to dashboards to interactive exploration tools.

Horkheimer and Adorno’s core thesis (1944): reason and rational control can turn into their opposite – into domination, normalization, oppression – when they become ends in themselves rather than means for human liberation.

In the context of Critical Theory: the expansion of human agency and self-determination – as a benchmark for social systems. In the AI context: the question of whether a system expands judgment or replaces it through dependency.

A feature flag is a toggle in the code that allows new features to be selectively enabled or disabled – without redeploying the code. Teams use feature flags to release new features to a small group of users first and instantly roll back if problems occur. In agent-driven workflows, they serve as a central safety net for continuous deployment.

Method of Critical Theory: a system is measured not against external norms but against its own promises and claims. Targets the tension between self-representation and reality.

A strategic competitive advantage that durably protects a company from competition – like a castle moat. Controversial for AI models: if models are interchangeable, the moat is absent.

The two modes of attention in Weiser/Brown’s model. Periphery = unconscious perception (informs without cognitive cost). Center = conscious focus (requires active attention). Good technology shifts seamlessly between both.

A strategic approach where a company opens its technology as a platform for others to build on. Examples: App Store (Apple), GPT Store (OpenAI). Goal: value creation through an ecosystem rather than a single product.

The observation that the growing number of AI ethics guidelines does not lead to more ethical action. The existence of principles creates the impression of solved problems while the structural causes remain unchanged.

Hypothetical role profiles from a stronger generalization of today’s product roles (PM, PD, Dev). Thinker = strategy, framing, decision. Builder = implementation, prototyping, iteration.

The Software Development Lifecycle describes the classic sequence of phases in software development: gather requirements, design the system, write code, test, review, deploy, and monitor. Traditionally, these steps run sequentially, with clear handoff points between each phase. In practice, many teams are seeing these phases dissolve – especially when AI agents execute large parts of the cycle simultaneously.

Test-Driven Development is a development method where automated tests are written first, followed by the code that should pass those tests. The cycle: write test, write code, pass test, clean up code. With AI agents, TDD increasingly becomes default behavior – the agent generates tests and code simultaneously.

Concept by Andrew Feenberg (1991). The implicit value decisions inscribed in a technical system’s architecture – before any explicit ethics policy. Makes visible that “neutral” design decisions have political and social consequences.