Deploying and Scaling in Production

Create an n8n AI Agent (or API-based agent) with at least three tools, including one that reads data and one that takes an action with a guard (limit or confirmation). Done when the agent correctly chooses different tools for different requests and refuses or escalates an action that exceeds its guard.

Build a chatbot that retrieves from a vector store of your real documents and answers only from retrieved context, citing the source and admitting when it does not know. Done when it answers an in-docs question with a citation and correctly declines an out-of-docs question instead of inventing an answer.

Add full tracing to an existing AI automation (input, output, model, tokens, cost, latency), then apply at least two cost optimizations from the lesson. Done when you can show a before-and-after cost per run and prove quality held using your eval set.

Build an eval set of at least fifteen real inputs with known-correct outputs. Make a prompt or model change, run the set before and after, and record the score delta. Done when your eval correctly flags whether the change helped or regressed.

Away from the screen, present your AI automation or agent to a business owner or manager who would actually use it. Explain what it does, what it costs to run, and how it fails safely. Done when they either commit to trying it or give you a specific reason they will not, which is just as valuable.