How to Use AI Assistant and AI Build to Assemble Your First Agent

An AI Agent builder turns a business goal into a structured Agent with a role, task, instruction, Skill chain, Source Data fields, and run policy. For many office users, the repetitive and manual work is not asking an AI one question. The real bottleneck is explaining the same workflow again, copying inputs, checking files, and confirming external actions every time. The Axon getting-started tutorial demonstrates a cleaner path: use AI Assistant to confirm the goal, use AI Build to fill the Agent form, then run the Agent manually before scheduling.

This article does not only restate the tutorial. It generalizes the method so it can support many future workflows: research delivery, meeting preparation, email attachment handling, daily reports, and recurring summaries. The first principle is consistent: do not ask the model to improvise an entire process. Convert the goal into a saved, inspectable, and runnable Agent.

Before building, it helps to understand What Are System Skills? The Foundation of an Axon AI Workforce, because a reliable Agent depends on executable capabilities, Source Data, and permission boundaries working together. For the input design layer, continue with Source Data Fields: Turning One-Off Prompts into Reusable Agents.

A good Agent is not a long prompt. It is a clear combination of goal, steps, inputs, outputs, and risk boundaries.

Start from the Business Goal, Not the Feature List

New users often begin by asking what the product can do. That is useful, but it is not the best starting point for building an Agent. A stronger starting point is a specific job:

• What work should be finished?
• What input is needed each time?
• What artifact should be produced?
• Which actions require human confirmation?
• How can the result be inspected?

The beginner tutorial uses a focused job: generate a public research report, export it as a PDF, and send it to a specified email address. This is a strong example for an AI Agent builder because it has a clear input, a sequence of steps, a file output, and an external action that must be reviewed.

AI Assistant Confirms the Goal

In the tutorial, the user opens the smart build flow and starts in the AI Assistant tab. The purpose is not to fill the form immediately. The purpose is to confirm the business goal and the capability combination. A practical prompt is:

I want to generate a public research report, export it as a PDF, and send it to a specified email address. How should I build the Agent?

AI Assistant helps turn that goal into a build instruction. This stage is useful for three questions:

1. Which System Skills are needed?
2. What does each step take as input and produce as output?
3. Which steps carry risk and need confirmation or a Trust Mode boundary?

Skipping this stage often produces vague Agents. The user may write "automate the whole report," but leave out topic input, file name, recipient, output language, and confirmation policy.

AI Build Fills the Form

After the goal is confirmed, the user switches to AI Build. AI Build extracts the structured Agent configuration: role, description, task, instruction, Skills, Source Data fields, and run policy.

The tutorial uses the role name Public Research Report Delivery. That is better than a generic name such as report assistant. A role name should sound like a business capability, not a chat persona. Clear naming also helps a team understand what an Agent is allowed to do.

What to Review in the Agent Form

Review the Role and Task

A runnable Agent should answer four questions:

Field	Review Point
Role	Does it describe a business capability?
Description	Does it explain the artifact and delivery method?
Task	Does it describe what each run should complete?
Instruction	Does it include source discipline, permission limits, and review rules?

An AI Agent builder result should not be accepted just because the fields are filled. The user should confirm that the form can support a real run. If the task is vague, research, PDF generation, and email sending may blur together during execution.

Keep the First Skill Chain Short

The tutorial uses exactly three steps:

1. std-internet-research.deep-research-flash
2. std-office-pdf.generate
3. std-internet-email.send_email

This chain is short but complete. It generates content, turns the content into a file, and handles external delivery with confirmation. For a first Agent, do not add too many steps. More steps mean more parameters, more failure points, and harder debugging.

Review the Source Data Fields

The tutorial keeps Source Data fields simple:

agent_goal: the business result for this run
source_material: the files or links the Agent may use
output_artifact: the expected draft or file type
reviewer: the person who reviews the first run
risk_boundary: the action that must pause for confirmation
first_run_mode: manual_review

These fields cover the main variables of an Agent build. They also make the Agent reusable: the user can change the goal, material, artifact, and reviewer from the execution form without rebuilding the Agent.

A Reusable Agent Build Method

Step 1: Write the Business Outcome

Avoid starting with "I need an intelligent assistant." Write the outcome instead:

I want to turn a public research topic into a PDF report and send it to a specified recipient.

This kind of goal includes the object, artifact, and delivery channel. It is much easier for AI Assistant to convert into a build instruction.

Step 2: List the Steps

Break the goal into three to five executable actions:

1. Research the public topic.
2. Produce Markdown.
3. Render the Markdown as a PDF.
4. Show a confirmation card before sending email.
5. Save or display the execution result.

This step turns the Agent from a chat request into a workflow.

Step 3: Mark the Risk Boundary

External sending, deleting, moving, publishing, and editing important files should be treated as high-risk actions. The tutorial keeps email behind a confirmation card. That makes the boundary visible before automation is enabled.

Step 4: Let AI Build Fill the Form

Once the goal, steps, and risks are clear, AI Build can produce a more reliable form. The input is no longer a vague wish. It is a structured work order.

Manual Testing Is Part of the Build

After saving the form, do not enable a schedule immediately. Go to the execution page and run the Agent manually. Check:

• Whether Research produces Markdown that stays on topic.
• Whether the PDF file card appears and opens in preview.
• Whether email sending pauses for confirmation.
• Whether the review inbox receives the correct attachment.
• Whether the Agent avoids private files, destructive actions, and unverified recipients.

If these checks fail, the Agent is still in the build phase. A saved form is not the finish line. A successful manual run is.

Common Mistakes

Mistake 1: Treating AI Assistant as the Final Builder

AI Assistant is for goal confirmation and build instruction preparation. The actual structured configuration is created in AI Build and then saved as an Agent form.

Mistake 2: Using a Generic Role Name

Names like research assistant or email bot do not explain business scope. Names like Public Research Report Delivery, Meeting Prep, or Daily Brief Orchestration are easier to govern.

Mistake 3: Forgetting Source Data Fields

Without Source Data fields, the Agent becomes a prompt that must be rewritten every time. These fields are what turn a one-time request into a reusable workflow.

FAQ

Q1: Does AI Build mean the user does not need to review the Agent?

No. AI Build structures the form, but the user still reviews the role, steps, Source Data fields, and permission boundaries.

Q2: How many steps should the first Agent have?

Three to five steps is usually enough. The tutorial uses Research, PDF, and Email because the chain is complete and easy to verify.

Q3: When should scheduled execution be enabled?

Only after a manual run passes review. For email workflows, confirm the recipient, subject, body, and attachment before any scheduled run.

Next Step

Start with the Axon beginner tutorial and build the Public Research Report Delivery Agent. After that, read How Research, PDF, and Email Become a Reviewable AI Workforce Workflow and How Trust Mode Protects Email Boundaries in an AI Workforce, then get started on the same builder method for meeting prep, daily reporting, document handling, or other repeatable office workflows.