Build a Support Agent with Vercel AI SDK – Full Tutorial

Why It Matters

It shows how modern AI toolkits can turn a typical support function into an autonomous, data‑driven service, accelerating cost savings and customer satisfaction for businesses adopting AI agents.

Summary

The video walks viewers through a step‑by‑step tutorial on building a production‑grade customer‑support AI agent using the Vercel AI SDK, OpenAI’s models, and a Supabase vector store. It frames the project as a concrete example of the emerging class of “AI agents” that go beyond static chatbots by making autonomous decisions—routing queries to internal knowledge bases or invoking live web searches when the answer isn’t found in the documentation.

Key technical insights include the use of retrieval‑augmented generation (RAG) to mitigate hallucinations, the creation of embeddings with OpenAI’s text‑embedding‑3‑small model, and the storage of those embeddings in a PostgreSQL‑based vector database (Supabase with the pgvector extension). The tutorial also covers intent classification logic, function‑calling patterns, and the integration of real‑time web search APIs, illustrating how the agent can dynamically choose the appropriate data source for each user request.

The instructor, Mayer Oshin—co‑author of O’Reilly’s *Learning Langchain*—demonstrates the workflow with a live example: a user asks how to join the Scrimba Discord, and the agent pulls the answer from Scrimba’s help docs, displaying source citations. When a question falls outside the docs, the same agent seamlessly switches to a web‑search routine, retrieving up‑to‑date information. Throughout, Oshin emphasizes hands‑on challenges, such as configuring OpenAI and Supabase API keys, generating and storing embeddings, and writing the SQL to enable pgvector.

For enterprises, the tutorial signals that sophisticated support automation is now accessible with relatively low‑code toolkits. By offloading routine inquiries and providing real‑time, source‑backed answers, companies can reduce support costs, improve response speed, and free human agents to focus on complex issues—all while gathering interaction data that can surface product insights and emerging trends.

Original Description

Use the popular Vercel AI SDK to create and ship a customer support agent that makes autonomous decisions to either answer questions based on your support docs or search the web in real time.

✏️ Study this course interactively on Scrimba:

https://scrimba.com/build-a-support-agent-with-vercel-ai-sdk-c0lk05ahir?utm_source=youtube&utm_medium=video&utm_campaign=fcc-vercel

Vercel AI SDK is a TypeScript-first toolkit for building AI features. It streamlines text generation, embeddings, and structured outputs. It plays nicely with tools, so your agent can call functions like “search the web” or “query my knowledge base” safely and predictably.

Scrimba on YouTube: https://www.youtube.com/c/Scrimba

⭐️ Contents ⭐️

0:00:00 Customer Support AI Agent Intro

0:06:47 Retrieval Augmented Generation Refresher

0:10:05 Embeddings Recap

0:12:57 Vector Database Embed Documents

0:20:19 Vector Database Retrieval

0:28:00 Challenge: Vector Database Retrieval

0:32:51 Vector Database Text Splitting and Retrieval

0:40:34 Vercel AI SDK Basics

0:43:51 Challenge: Vercel AI SDK Basics

0:47:22 Vercel AI SDK Structured Outputs

0:52:38 Challenge: Vercel AI SDK Structured Outputs

0:58:39 Vercel AI SDK Tool Calling

1:08:37 Challenge: Vercel AI SDK Tool Calling

1:18:04 Agentic Retrieval Routing

1:31:52 Challenge: Agentic Retrieval Routing

1:35:08 Web Search AI Agent

🎉 Thanks to our Champion and Sponsor supporters:

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