Quickstart: Build your first AI bot
Go from zero to a working AI bot in under 5 minutes. No code required.
What you'll build
In this quickstart, you'll create a web research bot — an AI agent that takes a topic, searches the web, and delivers a structured summary. It takes about 5 minutes.
By the end, you'll understand the three building blocks of every MagOneAI bot:
- Agent — the AI persona that reasons and acts
- Workflow — the sequence of steps the agent follows
- Execution — a single run of the workflow with real input
Prerequisites
Before you start, make sure you have:
- Access to a MagOneAI instance (Studio portal)
- An LLM provider configured (GPT-4o, Claude, or any OpenAI-compatible model)
- The Web Search tool available in your project
Don't have Web Search configured yet? Check the Web Search setup guide — it takes about 2 minutes.
Step-by-step
Create a project
If you don't have a project yet:
- Open MagOneAI Studio
- Click New Project
- Give it a name (e.g., "My First Bots")
All your agents, workflows, and executions live inside a project.
Create a workflow (Use Case)
- Go to Use Cases in the left sidebar
- Click Create Use Case
- Name it "Web Research Bot"
- Open the Canvas (workflow builder)
A Use Case is your workflow — and agents are created inside it.
Add an Agent node and configure it
Your workflow needs just three nodes:
START --> Agent (Web Researcher) --> END- The Start and End nodes are already on the canvas
- Drag an Agent node from the sidebar onto the canvas
- Connect: Start → Agent → End
- Click the Agent node to open its properties panel
- Configure the agent:
| Field | Value |
|---|---|
| Name | Web Researcher |
| Role | Research analyst |
- Set the persona instructions:
You are a research analyst. Given a topic, search the web for current,
accurate information and produce a structured summary.
Your output must include:
- A 2-3 sentence overview
- 3-5 key facts or findings
- Sources you referenced
Be concise and factual. Cite your sources.- Under Tools, add the Web Search tool
- Enable Can execute tools
- Click Save
That's it — your agent and workflow are ready to run.
Run your bot
- Click Execute (or Run) on the canvas
- Enter your input:
{
"query": "What are the latest developments in quantum computing?"
}-
Watch the execution progress:
- Start node processes input
- Agent node calls the LLM, which searches the web and synthesizes results
- End node returns the final output
-
View the result — a structured research summary with sources
Review the execution
After the run completes:
- Go to Executions in the sidebar
- Click on your execution
- Explore:
- Activity logs — see input/output for each step
- LLM usage — tokens used and cost
- Duration — how long each activity took
Every execution is fully logged and auditable.
What just happened?
Here's what MagOneAI did under the hood:
Your execution runs on Temporal — the same engine used by Uber and Stripe. If the server crashed mid-execution, it would resume exactly where it left off.
The LLM received your persona instructions, the user's input, and the list of available tools. It decided to call the web search tool with relevant search queries.
The agent called the Web Search MCP server, which returned search results. The agent then synthesized these results into a structured summary — all within a single Agent node.
MagOneAI uses DSPy to ensure the agent produces structured, validated output — not just raw text. This makes outputs reliable and machine-readable.
Make it more powerful
Now that you have a working bot, here are quick ways to level it up:
Add conditional logic
Route to different agents based on the query type — questions go one way, research requests go another
Add human approval
Pause the workflow for human review before sending results to a customer
Run agents in parallel
Research multiple topics simultaneously — 3 agents running at the same time
Process files and data
Upload CSVs or PDFs and have agents analyze, summarize, or review them
Next: Build more bots
Ready to build something more advanced? Check out these quick bot recipes — each one takes under 10 minutes: