Vom Prompt zum Loop: Die Evolution der KI-Steuerung · Part 4 of 8
Das Harness und die Umgebung
Harness Engineering, Leitplanken und automatische Evaluatoren
The environment decides whether an agent is production-ready: guardrails, sandboxes and automatic evaluators.
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About this audiobook
The success of an AI application is decided less by the model you pick than by the harness around it. Part 4 is about that environment: guardrails that catch risky output before it reaches a user, sandboxes and Git worktrees for controlled experiments, and evaluators that check results automatically.
It shows how linters, tests and LLM-based evaluators fit together, and how to balance an agent's freedom against the control a production system needs.
Published in German, 36 minutes. For developers who want reliable, verifiable AI applications instead of impressive demos.
Chapters
- Warum Agenten Leitplanken brauchen
- Sandboxen, Worktrees und Evaluatoren
- Freiheit und Kontrolle im Systemdesign
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See the courseVom Prompt zum Loop: Die Evolution der KI-Steuerung
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1. Das Handwerk der WorteRoles, few-shot examples, chain of thought and system instructions: prompting as a craft, not a guessing game.
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2. Kontext ist KönigHow to give a language model the right knowledge: context windows, memory and retrieval-augmented generation.
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3. Die Entstehung der AgentenFrom answering machine to actor: tool use, function calling, ReAct and the Model Context Protocol.
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5. Die Geburt von Loop EngineeringPlan, execute, observe, refine: why autonomous work cycles beat single prompts on every long task.
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6. Anatomie eines perfekten LoopsStop conditions, context rotation, memory and a human emergency stop: what keeps an autonomous loop safe.
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7. Verifizierung und Self-CorrectionA model must not grade its own work: generator-evaluator loops, hard tests and fallbacks for AI you can trust.
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8. Das autonome ÖkosystemTeams of specialised agents, hierarchy versus swarm, and the human as architect, auditor and conductor.