> ## Documentation Index
> Fetch the complete documentation index at: https://helpcenter.magure.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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:

1. **Agent** — the AI persona that reasons and acts
2. **Workflow** — the sequence of steps the agent follows
3. **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

<Tip>
  Don't have Web Search configured yet? Check the [Web Search setup guide](/tools/web-search) — it takes about 2 minutes.
</Tip>

***

## Step-by-step

<Steps>
  <Step title="Create a project">
    If you don't have a project yet:

    1. Open **MagOneAI Studio**
    2. Click **New Project**
    3. Give it a name (e.g., "My First Bots")

    All your agents, workflows, and executions live inside a project.
  </Step>

  <Step title="Create a workflow (Use Case)">
    1. Go to **Use Cases** in the left sidebar
    2. Click **Create Use Case**
    3. Name it "Web Research Bot"
    4. Open the **Canvas** (workflow builder)

    A Use Case is your workflow — and agents are created inside it.
  </Step>

  <Step title="Add an Agent node and configure it">
    Your workflow needs just three nodes:

    ```
    START --> Agent (Web Researcher) --> END
    ```

    1. The **Start** and **End** nodes are already on the canvas
    2. Drag an **Agent** node from the sidebar onto the canvas
    3. Connect: **Start** → **Agent** → **End**
    4. Click the **Agent** node to open its properties panel
    5. Configure the agent:

    | Field    | Value            |
    | -------- | ---------------- |
    | **Name** | Web Researcher   |
    | **Role** | Research analyst |

    6. 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.
    ```

    7. Under **Tools**, add the **Web Search** tool
    8. Enable **Can execute tools**
    9. Click **Save**

    That's it — your agent and workflow are ready to run.
  </Step>

  <Step title="Run your bot">
    1. Click **Execute** (or **Run**) on the canvas
    2. Enter your input:

    ```json theme={null}
    {
      "query": "What are the latest developments in quantum computing?"
    }
    ```

    3. 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

    4. View the result — a structured research summary with sources
  </Step>

  <Step title="Review the execution">
    After the run completes:

    1. Go to **Executions** in the sidebar
    2. Click on your execution
    3. 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.
  </Step>
</Steps>

***

## What just happened?

Here's what MagOneAI did under the hood:

<AccordionGroup>
  <Accordion title="1. Temporal created a durable workflow" defaultOpen>
    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.
  </Accordion>

  <Accordion title="2. The agent reasoned about your query">
    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.
  </Accordion>

  <Accordion title="3. Tools executed automatically">
    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.
  </Accordion>

  <Accordion title="4. Structured output was produced">
    MagOneAI uses DSPy to ensure the agent produces structured, validated output — not just raw text. This makes outputs reliable and machine-readable.
  </Accordion>
</AccordionGroup>

***

## Make it more powerful

Now that you have a working bot, here are quick ways to level it up:

<CardGroup cols={2}>
  <Card title="Add conditional logic" icon="code-branch" href="/workflows/condition-node">
    Route to different agents based on the query type — questions go one way, research requests go another
  </Card>

  <Card title="Add human approval" icon="user-check" href="/workflows/human-task-node">
    Pause the workflow for human review before sending results to a customer
  </Card>

  <Card title="Run agents in parallel" icon="arrows-split-up-and-left" href="/workflows/parallel-node">
    Research multiple topics simultaneously — 3 agents running at the same time
  </Card>

  <Card title="Process files and data" icon="file" href="/tools/database-tools">
    Upload CSVs or PDFs and have agents analyze, summarize, or review them
  </Card>
</CardGroup>

***

## Next: Build more bots

Ready to build something more advanced? Check out these quick bot recipes — each one takes under 10 minutes:

<CardGroup cols={2}>
  <Card title="5 bots you can build today" icon="bolt" href="/cookbooks/quick-bots">
    Step-by-step recipes for support router, company research, competitor analysis, CSV analyzer, and document reviewer — no OAuth setup needed
  </Card>

  <Card title="Sales intelligence assistant" icon="briefcase" href="/cookbooks/sales-intelligence">
    A full multi-agent workflow with parallel research — the most impressive demo
  </Card>
</CardGroup>