Vom Prompt zum Loop: Die Evolution der KI-Steuerung · Part 8 of 8
Das autonome Ökosystem
Multi-Agenten-Orchestrierung und die Zukunft der KI-Arbeit
Teams of specialised agents, hierarchy versus swarm, and the human as architect, auditor and conductor.
Listen on
Preview on Spotify
About this audiobook
One loop solves one task. Part 8, the finale, is about what happens when specialised agents divide work among themselves: how they communicate, when a central manager agent beats a swarm, and where orchestration fails.
The second half is about you. Your role shifts from executor to architect, auditor and conductor of automated processes, and the audiobook gives a roadmap for bringing that into a company or a project.
Published in German, 38 minutes. For product people, leads and anyone deciding how AI work gets organised.
Chapters
- Warum einzelne Loops an Grenzen stoßen
- Hierarchie, Schwarm und Kommunikation
- Der Mensch als Auditor und Dirigent
Want to apply this in your own business?
The audiobooks explain the ideas. In the Kickstart workshop your team builds its first tool in one day.
See the courseVom Prompt zum Loop: Die Evolution der KI-Steuerung
-
1. Das Handwerk der WorteRoles, few-shot examples, chain of thought and system instructions: prompting as a craft, not a guessing game.
-
2. Kontext ist KönigHow to give a language model the right knowledge: context windows, memory and retrieval-augmented generation.
-
3. Die Entstehung der AgentenFrom answering machine to actor: tool use, function calling, ReAct and the Model Context Protocol.
-
4. Das Harness und die UmgebungThe environment decides whether an agent is production-ready: guardrails, sandboxes and automatic evaluators.
-
5. Die Geburt von Loop EngineeringPlan, execute, observe, refine: why autonomous work cycles beat single prompts on every long task.
-
6. Anatomie eines perfekten LoopsStop conditions, context rotation, memory and a human emergency stop: what keeps an autonomous loop safe.
-
7. Verifizierung und Self-CorrectionA model must not grade its own work: generator-evaluator loops, hard tests and fallbacks for AI you can trust.