Mehrere KI-Agenten, Tools und Workflows koordiniert zusammenspielen lassen — von MCP-Servern über Keyword-gesteuerte Abläufe bis zu Multi-Agent-Pipelines.
A2A is an open protocol for communication between independent AI agents across vendors — how it differs from MCP, and use cases in multi-agent systems.
How tracing makes agent steps and tool calls visible as spans — the basis for debugging and cost control, with tools like LangSmith and Langfuse.
The difference between fixed, predefined workflows and autonomous AI agents — with Anthropic's definition, the trade-offs, and a clear decision guide.
One AI agent generates, a second one evaluates and critiques — looping around until the result meets a clear quality bar.
What human-in-the-loop means in agent workflows: approval gates, intervention points before critical actions, and why they are mandatory for irreversible steps.
What an LLM router does: send requests to cheap or strong models automatically, cut costs, and understand the risks when it misclassifies.
MCP is the open standard that connects AI models to external tools and data sources. Here is how its client-server architecture works.
How multiple AI agents work together: orchestrator-worker, supervisor pattern, task splitting and synthesis, and when multi-agent actually pays off.
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.
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.
How agent workflows save state after every step (checkpointing) and resume exactly where they stopped after a crash — durable execution explained.
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.