Knowledge, Context and Agentic Engineering for Knowledge Work

Knowledge, Context and Agentic Engineering for Knowledge Work

This teaching line examines how Models trained on extensive text corpora to process instructions and generate context-dependent language. operate inside Working environments that connect a language model with project context, files, tools and controlled actions. for computer-based, data-driven knowledge work. Its five modules connect model foundations with The deliberate formulation of instructions for a specific model, task and intended output., Maintaining explicit project knowledge and assembling the information a model needs for the current task., Organising extended work across tool use, intermediate results, permissions, feedback and human review., and Examining technical limits and social consequences, and defining responsibility and rules for use.. The shared Full Slide Deck, two Full Lecture Notes language versions and an ordered Video Playlist carry the material; each workshop selects from these sources and adapts them to a defined field, task and teaching language.

Digital Humanities Craft develops and maintains these public teaching materials. Commissioned workshops fund their ongoing revision, and reusable results return to the shared material. Sources and revision history are available on GitHub.

Teaching Materials

Full Slide Deck

A complete overview of the teaching material in one slide deck. It serves as a reference and as the source for workshop-specific decks.

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Full Lecture Notes

The written-out version of the slides, with explanations and context for reading and reference.

English

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Deutsch

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Preparation

The preparation slides cover Python, Visual Studio Code and an AI Harness, with a practical setup exercise. The slides are in English.

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Video Library

The Full Video Playlist contains the current maintained sequence. Opening a module that contains videos can load preview images from YouTube.

01 Understanding Large Language Models (5 videos)
  1. Why AI Tokens are so Expensive
  2. Wie LLMs funktionieren
  3. Deep Dive into LLMs like ChatGPT
  4. What is Multimodal AI? How LLMs Process Text, Images, and More
  5. Just Go Local Solves Nothing. Here's What Does
02 Prompt Engineering (1 video)
  1. Prompt Engineering
03 Knowledge and Context Engineering (6 videos)
  1. What Is Context Engineering? Why It Matters for AI Agents
  2. Thinner Agents on a Smarter Substrate: The Ontology-based Semantic Layer
  3. Why Agentic Systems Need Ontologies
  4. Citation Needed: Provenance for LLM-Built Knowledge Graphs
  5. Wissens- und Projektmanagement mit Obsidian und Claude Code
  6. Grounded Vault: Quellenbasiertes Arbeiten mit AI Agents
04 Agentic Engineering (22 videos)
  1. 5 AI Agent Terms You Need to Know
  2. AI Model vs Agentic Harness: What Actually Drives AI
  3. MCP vs Skills: Which Is Right for Your AI Agent and LLMs?
  4. 5 Best Practices for Building AI Agent Skills
  5. Agentic Engineering vs Software Engineering: Beyond Vibe Coding
  6. The Best Local Agentic Coding Workflow (Complete Guide)
  7. What I Use Instead
  8. Meet Pi: The Minimalist, Self-Modifying Coding Agent
  9. Building Pi, and What Makes Self-Modifying Software So Fascinating
  10. I Hated Every Coding Agent, So I Built My Own (Pi)
  11. Einführung in Promptotyping (Teil 1)
  12. Einführung in Promptotyping (Teil 2, Live Demo mit Claude Code)
  13. Anthropic's Dynamic Workflows: What Everyone Gets Wrong
  14. Claude Can Now Build Its Own Harness for Every Task
  15. Agentic AI and the Future of Software Development
  16. Agentic Engineering für digitale Editionen mit Claude Code, TEI und GND
  17. Agentic Engineering und digitale Edition mit Claude Code
  18. Research Mission Control: Eine Forschungsleitstelle mit mehreren KI-Agenten
  19. How to Use Multiple AI Agents in Your IDE
  20. This Meta-Harness Changes How You Run AI Agents
  21. Loop Engineering: Why Everyone Is Talking About Agentic Loops
  22. Stop Building AI Agents the Old Way
05 Critical Perspectives and Governance (7 videos)

Open Full Slide Deck

  1. Matrix 2025: Gefangen in der Kalifornischen Ideologie – scobel
  2. Douglas Rushkoff – das Mindset der Tech-Milliardäre | Sternstunde Philosophie | SRF Kultur
  3. Die geheime Steuerung, die niemand wählte – scobel im Gespräch: Mit Anna-Verena Nosthoff
  4. Kybernetik kurz erklärt | 5 Fragen an Anna-Verena Nosthoff
  5. What the hell happened with AGI timelines in 2026?
  6. Where AGI timelines go wrong | Toby Ord, Oxford University
  7. Can $500 Billion Win the AI Race? | Anton Leicht