n8n Slack Bot + AI Agent: Answer Team Questions from Your Docs
Your team asks the same question in Slack every week. Wire a Slack Trigger to an n8n AI agent with a vector store and the bot answers from your own documents, in the thread, in seconds.
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118 tested guides on AI agents, n8n, RAG, and automation — newest first.
n8n Code Node in AI Workflows: When the Built-In Nodes Are Not Enough
The built-in AI nodes cover the common cases. But batching rows, reshaping API responses, and building scoring functions hit a wall fast. The Code node fills the gap with JavaScript wherever the visual nodes cannot reach.
n8n AI Lead Enrichment: Score and Route Every New Lead Automatically
A new lead arrives with nothing but a name and email. An n8n workflow pulls company data, scores the lead with an AI agent against your ICP, and routes it to the right rep or drip sequence before anyone opens a browser tab.
n8n AI Feedback Analysis: Classify Sentiment and Route Urgent Complaints Automatically
Customer feedback arrives from five places and nobody reads most of it. An n8n workflow with an AI agent classifies every piece by sentiment and theme, routes urgent complaints immediately, and builds a weekly digest your product team can act on.
n8n Google Drive AI Document Processing: Summarize and Extract Data Automatically
A contract lands in a shared Drive folder and sits unread for three days. An n8n workflow detects the file, extracts the text, runs it through an AI agent, and posts a structured summary with key dates and parties before anyone opens it.
LLM Context Windows: Tokens, Limits, and Lost in the Middle
A context window is the token budget for one request, and input plus output share it. Bigger windows hold more text but do not read it all equally: the lost-in-the-middle effect means facts buried mid-context get recalled worse. Here is how tokens, limits, and placement actually
n8n Expressions Explained: Items, $json, and Node References
Most n8n frustration is a data-shape misunderstanding, not a broken node. Every connection carries an array of items, each with a json key. Once you can picture that shape, $json, $('Node Name'), and the 'Referenced node has no data' error all make sense.
Build an AI Meeting-Notes Agent in n8n: Transcribe to Actions
You record every call and still lose the decisions. This is an honest, end-to-end n8n build that turns a meeting recording into structured notes plus routed action items, with straight talk on the Whisper file limit, missing speaker labels, per-minute cost, and consent.
AI Agent Observability: Tracing, Metrics, and Cost in Production
Your agent gave a wrong answer and you have no idea where it broke. Observability captures the run (every LLM call, tool call, prompt, and cost) so you can replay it and point at the exact failing step. Here are the three pillars, what to log per step, and when a dedicated tool
Contextual Retrieval: Fix RAG Chunks That Lose Context
Plain RAG embeds chunks that have lost their document context, so 'revenue grew 3%' matches nothing useful. Contextual retrieval writes a short per-chunk context and prepends it before embedding and BM25. Here is the method, the build, and the honest ingest trade-off.
LLM Temperature and Top-P: What the Sampling Settings Do
You turned the temperature up and the model got more creative, down and it got smarter. Both stories are wrong. Temperature and top-p only reshape how the next token is drawn. Here is what each setting really does, top-p versus top-k, and which value to use per task.
Semantic Kernel + AutoGen Merged: The 2026 Agent Framework Shift
Microsoft folded two of its biggest open-source agent projects, Semantic Kernel and AutoGen, into a single production SDK with version 1.0. Here is what the Microsoft Agent Framework bundles, where it sits against LangGraph, CrewAI, and OpenAI's Agents SDK, and how a small team