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Automation

How to build an AI agent

AI Agent Builder turns a plain-language job description into a workflow that can run on demand, on a schedule or when a message arrives. Each step can use the result from the step before it.

This guide covers the regular-user workflow for creating and monitoring an agent. Workspace installation, provider credentials and plan controls are intentionally excluded.

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When to use this

  • A repeated task requires several decisions or tools, not one generated answer.
  • You want the workflow to run on a schedule or react to incoming messages.
  • You need a visible run history before trusting an automation.

Start from the Agent Builder

  1. Open the agent dashboard

    Open AI Agent Builder. Start with the plain-language description box, open the visual builder, or choose a featured starting point such as a daily report, weekly summary or morning briefing.

    AmmarAI Agent Builder dashboard with a plain-language agent description field and featured workflows
    Choose a starting point for the workflow.

  2. Connect only the services the job needs

    Open Manage Connectors, select Add Connector and link the account needed by this workflow. Keep access narrow: an email workflow needs its mailbox, while an unrelated service can remain disconnected.

    AmmarAI Agent Builder dashboard with the Manage Connectors action highlighted
    Open the connector manager from the agent dashboard.

  3. Choose the relevant connector

    Select the service the agent should use. The available actions shown for each connector make it clear whether the agent can read messages, create drafts, send mail, manage contacts or update a calendar.

    Agent connector picker showing email services and their available actions
    Choose a connector by the actions your workflow requires.

Describe and refine the workflow

  1. Describe the complete job

    State what should happen, when it should run and where the result belongs. For example: every weekday morning, summarize yesterday’s messages and send the briefing to a chosen channel. Continue the conversation to refine the workflow.

    AmmarAI Agent Builder description box and workflow starting options
    Describe the goal, timing and destination in one clear brief.

  2. Choose when the agent starts

    In the Visual Builder, select the Trigger node. Choose Schedule for a recurring job, using a preset or a custom schedule. Choose Channel Message when the workflow should react to an incoming message on a connected channel. Check that the trigger matches your brief before adding actions.

  3. Add and connect the actions

    Use + to open Add Step. Choose actions such as Ask Agent, Send Message, Generate Report, Gmail or Outlook according to the job. Put them in execution order and use the previous step’s output where needed. For an email briefing, read the messages first, summarize them next and send the final report last. Use a Path step when the next action depends on a condition.

  4. Test, save and activate

    For a Channel Message workflow, use Test Agent to send a sample message and inspect the result. Correct vague instructions or missing connections, then Save and set the agent Active. For scheduled work, review the first scheduled run in the agent’s history. Pause the agent while correcting an unexpected result.

Monitor agents and conversations

  1. Review every agent from one list

    Open Agents to compare statuses and last-run times. Use the options menu to edit, pause or remove a workflow, or start a conversation with an agent directly.

    AmmarAI agents list with status, last-run information and agent options
    Check status and recent activity across all agents.

  2. Inspect messages and run details

    Open Channel Messages and filter by channel to find a conversation. Read the full history, then use Details and Logs to check the configuration, incoming message, execution path and output. Reply directly when a conversation needs your attention. Review the first runs before expanding the agent’s responsibility.

    AmmarAI agent conversation with Details and Logs for a completed workflow
    Use the conversation history and logs to audit each run.

Ready to try it?

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