Workshop Index

Workshop Chapters

Arbeitsübersicht der Kapitel und Unterthemen aus D:\course\chapters.

Chapter 1: Foundations — What's Actually Happening

  • The Agentic Loop
  • What is an LLM
  • Tokens — input vs output
  • Pricing — API vs subscription
  • Model vs Harness
  • Agents are just 4-5 tools in a loop
  • 12-Factor Agents — key principles
  • The two levers that actually matter — fewer steps OR more accurate step selection

Chapter 2: The Landscape — Where Are We and Where Is This Going

  • Stages of Agentic Engineering
  • Vibe Coding vs Agentic Engineering
  • Danger of CEOs — AI is imperfect but very capable
  • Models will change — your moat is evals, harness engineering, and mental models

Chapter 3: Getting Your Hands Dirty — Setup and First Loop

  • Prerequisites — Node.js, GitHub account, API key
  • /setup page on Sievering
  • Give them a CLI to test
  • Git repos — clone vs own
  • Why CLI over GUI
  • Permissions in Claude
  • The first Ralph Loop

Chapter 4: Context Is Everything — The Core Skill

  • Context Window
  • Context Rot
  • The Dumb Zone
  • Plan Mode
  • Claude.md / AGENTS.md
  • MD files
  • Skills
  • .env
  • Artifact Files
  • Frequent Intentional Compaction
  • RPI framework and QRSPI evolution
  • Human Leverage Pyramid
  • "Don't read the plans, read the code"
  • Instruction budget

Chapter 5: Quality and Control — Making It Reliable

  • Backpressure
  • Tests
  • TDD for Agents
  • Subagents
  • Browser Use
  • Prompt Injection
  • "Control flow via prompt" anti-pattern
  • Consistency vs Variance tradeoff

Chapter 6: Scaling Up — Team and Architecture

  • Orchestrator Pattern
  • Remote Control
  • Reusable Skills
  • Team Sharing
  • CLI Loop
  • Library Meta-Skill
  • Background Agent Patterns
  • Coaching Loop

Chapter 7: Harnesses, Models, and Providers — Choosing the Right Stack

  • The stack map: application → harness → model → provider → hosting
  • Harness landscape
  • Why the harness matters
  • Model landscape
  • Provider landscape
  • One model, many providers
  • Data protection and procurement basics
  • EU-sensitive setups
  • Enterprise routes and self-hosted options
  • Trade-offs: compliance, latency, quality, cost, lock-in, observability, operational burden
  • Future-proofing with provider/model abstraction

Chapter 8: Codebase Poisoning — Why Messy Systems Teach Agents Bad Habits

  • What codebase poisoning means
  • Similarity to context poisoning
  • Brownfield reality
  • The danger of a dirty core
  • Good code is contagious, bad code is contagious too
  • Clean starts, protected cores, and golden paths
  • Refactor-before-scale
  • Scaffold the patterns you want copied
  • Constrain generation and use the harness to fight poisoning
  • Detect poisoning early
  • Brownfield strategy: isolate a clean seam

Chapter 9: Autoresearch — Agents Running ML Experiments, Not Just Writing Code

  • What Karpathy means by autoresearch
  • The core loop: modify code → run training → measure → compare → iterate
  • Minimal surface area
  • program.md as human-programmed research organization
  • Fixed wall-clock budgets and objective metrics
  • The research harness pattern
  • Human role shift
  • Failure modes and operational risks
  • Generalization beyond ML

Chapter 10: Vocabulary — Why Words Matter When Working With Agents

  • Why vocabulary is not pedantry
  • The communication problem in AI
  • LLM vs agent vs tool vs harness
  • Vocabulary as debugging, design, and collaboration tool
  • Why vague language leads to vague requests
  • Domain vocabulary matters too
  • Building a personal glossary
  • Teaching agents your vocabulary
  • Key distinctions: AI, LLM, Model, Agent, Tool, Harness, Context, Prompt, Eval

Agentic OS — Folder Structure as Agent Architecture

  • The pattern: a shared workspace folder is the agent's brain
  • Why it beats hosted / framework stacks
  • The four layers: Rules, Context, Skills, Memory / Learnings
  • Workspace structure for business use
  • Agent Runner: Pi
  • The core insight: folder structure is orchestration