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An n8n AI Agent that finishes without an error but ignores its tools, loops until it quits, or returns the wrong shape is almost always a configuration problem. Here are the 6 failure modes that cover most of them, and the specific node setting behind each fix.
Moonshot's Kimi K3 is a 2.8-trillion-parameter model with a 1-million-token context and an OpenAI-compatible API. That last part means it drops into an n8n AI Agent today. Here is how to wire it up, what it costs, and when it beats a frontier model.
Kimi K3, GLM-5.2, DeepSeek V4, Claude, and GPT all plug into an n8n AI Agent, and they are not interchangeable. Here are the 5 questions that decide the pick for your agent, and why the honest answer for most workflows is to route by task, not standardize on one.
A slow n8n AI Agent is usually doing more work than the task needs: too many tool loops, too big a context, or too heavy a model. Latency and cost come from the same place, the model calls, so here is how to measure them and the levers that cut both at once.
n8n has guides for Telegram and WhatsApp AI agents, but Discord is the gap, mostly because there is no Discord Trigger node. Here is how to build a Discord AI agent from 3 parts: a Webhook trigger, the AI Agent, and the Discord node to reply.
An n8n AI Agent that states a wrong fact or invents a tool result is hallucinating, filling a gap where it has no grounded answer. The fix is not a better model. It is giving the agent real data and constraining what it is allowed to say. Here is how, layered.
