AIQB
TutorialsOrdinary

How I Would Design an n8n AI System That Can Recover From Its Own Failures

Source: DEV Community·

Summary

The first time an n8n AI workflow fails, it often does not fail cleanly. It returns a 200 status...
CategoryAI Tutorials & Practice
TierOrdinary
Published
Indexed by AIQB
SourceDEV Community
AIQB record IDintel-daef355ce42d142cf83f8c0a
Read original source ↗More Tutorials

Related stories

Matched by category, entity tags and source

TutorialsOrdinary
n8n + RAG + MCP: Designing an AI Workflow That Knows Where Its Knowledge Comes From
The dangerous AI workflow is not the one that says, “I don’t know.” It is the one that gives a...
Source: DEV Community
TutorialsOrdinary
Your n8n Workflow Has 40 Nodes. Should Any of Them Be an AI Agent?
Open an n8n workflow with 40 nodes and two thoughts usually appear at the same time. First: This is...
Source: DEV Community
TutorialsOrdinary
The Retrieval Stack Nobody Shows You: Chunking, Reranking, Filtering, and Context
Most RAG failures are not model failures. The model did not forget how to read. The prompt is not...
Source: DEV Community
TutorialsOrdinary
When RAG Gets Bigger, Accuracy Doesn't Always Get Better
The first time a RAG system gets a large knowledge base, it often does not fail loudly. It gets...
Source: DEV Community
TutorialsOrdinary
RAG Has a Routing Problem: Knowing Which Knowledge Source to Trust
Your RAG system can retrieve the “right” document and still give the wrong answer. The document may...
Source: DEV Community
TutorialsOrdinary
The Retrieval Pipeline Is Lying to You: How RAG Fails Before the LLM Sees Anything
Your RAG system did not fail because the model hallucinated. It failed because the only “facts” the...
Source: DEV Community
TutorialsOrdinary
Your RAG Retrieved the Right Document — So Why Was the Answer Wrong?
You check the logs. The vector search returned the exact policy document the user asked about. The...
Source: DEV Community
TutorialsOrdinary
The Difference Between an AI Agent That Works and One You Can Trust
An AI agent that works can complete a task. An AI agent you can trust can do something harder: it...
Source: DEV Community
TutorialsOrdinary
The Agent Loop Nobody Talks About: Think, Act, Observe, Repeat
Most agent failures are not model failures. They are loop failures. The model may be smart enough....
Source: DEV Community
TutorialsOrdinary
An LLM judge cannot be a build gate, and it is not about the cost
Almost every RAG evaluation metric on offer needs a language model to produce it. Faithfulness,...
Source: DEV Community