Your first 30 minutes

In one line. One guided sit-down: build a small but real Agentic Search assistant over three documents, and meet every core idea on the way. You’ll need. A project you can build in, and three PDFs you know well — policy documents, product guides, anything your colleagues ask questions about.

Don’t read this chapter. Do it. Every concept you meet here — collection, agent, knowledge attachment, playground — carries the rest of the academy.

Minutes 0–5 · Look around

Sign in and you land on the Command Deck — the cross-workspace home (press Ctrl K to jump anywhere). Open your project from Jump back in or Browse everything, and you’re on the consumer surface: the workspace, chat, dashboards. This is what your users will get. Now find Studio in the navigation — Studio is where builders work, and your users will never see it. Take the two-minute tour in E1 · Getting oriented if you want names for what you’re seeing, then come back.

Minutes 5–10 · Create a Knowledge collection

Studio ▸ Data Model ▸ Collections ▸ + Collection

1. Name it something honest — HR Policies, Product Guides.
2. Set Category = Knowledge. This single choice is what makes it a Search collection: documents become readable knowledge the moment they land. No lifecycle, no stages.
3. Upload your three PDFs.

While they process, understand what you just did: you gave your future agent its ground truth. An agent can only answer from what you attach — that boundary is a feature, not a limitation.

Minutes 10–20 · Hire your first agent

Studio ▸ Agent Builder ▸ Agents ▸ + Agent

1. Persona — describe the worker in plain language: “You answer employee questions about our policies. Answer only from the attached documents, and always cite your sources. If the documents don’t contain the answer, say so.”
2. Model — pick a registered LLM, or leave Auto.
3. Knowledge — attach HR Policies. This is the load-bearing wire of the whole build: it is what turns a generic chatbot into your expert.

Save. That’s a hire.

Minutes 20–25 · Ask it things

Open the agent’s Playground and ask real questions — the ones people actually bring to your team. Watch what comes back: grounded answers with citations you can click through to the exact source passage.

Ask something the documents don’t cover. A well-instructed agent says it doesn’t know instead of inventing — if yours doesn’t, tighten the persona line about refusing and try again. This test-refine loop in the playground is how every agent on the platform gets good.

Minutes 25–30 · Give it to someone

Your users don’t open Studio — they open Chat. Publish the experience and send the link to one colleague with the message “ask it anything about our policies.” Watch what they ask. Their third question is usually the moment this stops being a demo.

What you just built

Agentic Search — solution shape 1 of 3. A Knowledge collection, one agent, one attachment, one shared experience. The other two shapes are the same two heroes doing bigger jobs:

• Add a schema, a lifecycle, and a review queue and the agent stops answering questions and starts processing work — that’s an Agentic Workflow.
• Add more collections, more agents, and governance across all of it — that’s an Agentic System.

Where to go next

• Make this build production-worthy: the Search pattern, then the full worked build in B1 · Policy assistant.
• Meet the agent editor properly: A3 · Your first agent.