Agentic Workflows vs. Agents — when to fix, when to free
The difference between fixed, predefined workflows and autonomous AI agents — with Anthropic's definition, the trade-offs, and a clear decision guide.
Redaktion
KI-Orchestrierung
Mehrere KI-Agenten, Tools und Workflows koordiniert zusammenspielen lassen — von MCP-Servern über Keyword-gesteuerte Abläufe bis zu Multi-Agent-Pipelines.
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- ki-orchestrierung Workflow-Automatisierung
Evaluator-Optimizer Loop
One AI agent generates, a second one evaluates and critiques — looping around until the result meets a clear quality bar.
Redaktion - ki-orchestrierung Workflow-Automatisierung
Human-in-the-Loop — Approval Gates for AI Agents
What human-in-the-loop means in agent workflows: approval gates, intervention points before critical actions, and why they are mandatory for irreversible steps.
Redaktion - ki-orchestrierung Keyword-Routing
LLM Routing and the LLM Router
What an LLM router does: send requests to cheap or strong models automatically, cut costs, and understand the risks when it misclassifies.
Redaktion - ki-orchestrierung MCP-Workflows
Model Context Protocol (MCP)
MCP is the open standard that connects AI models to external tools and data sources. Here is how its client-server architecture works.
Redaktion - ki-orchestrierung Multi-Agent
Multi-Agent Orchestration
How multiple AI agents work together: orchestrator-worker, supervisor pattern, task splitting and synthesis, and when multi-agent actually pays off.
Redaktion - ki-orchestrierung Agenten
Plan-and-Execute — the Planner-Executor Pattern for AI Agents
Plan-and-Execute means: build the full plan first, then work through it step by step. How the pattern works and where it beats ReAct on cost and quality.
Redaktion - ki-orchestrierung Workflow-Automatisierung
Prompt Chaining
Chaining several LLM calls into a pipeline: one step's output becomes the next step's input, gates as checks, and when chaining beats a mega-prompt.
Redaktion - ki-orchestrierung Agenten
The ReAct Pattern: Reasoning and Acting in Turns
How the ReAct pattern's Thought, Action and Observation loop works, why tool-using AI agents rely on it — and where it typically breaks down.
Redaktion