Agent Studio | Visual AI Agent Builder

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Overview

• Build agents visually - Create agents through an intuitive user interface
• Configure everything - Set up models, tools, knowledge bases, memory stores, evaluators, and safety guardrails.
• Publish for production - Deploy agents for use via API or Python or NodeJS SDK

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Getting Started

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Creating Your First Agent

1

Navigate to the Orq Studio

Open the Orq.ai Studio

2

Create a new Agent

Use the ’+’ button in your Project or Folder.

3

Configure your Agent Details

Name and Describe your Agent. You can choose to get assistance using AI to preconfigure agent role and instructions. Choose Start from scratch to configure your agent manually.The Agent Studio editor will open with a preconfigured or default template you can further customize.

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Understanding the Interface

• Instructions Panel (Left) - Define what your agent does and how it behaves
• Configuration Panel (Right) - Set up models, tools, context, evaluators, and constraints

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Configuration Guide

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Instructions Section

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Writing Effective Instructions

You are an experienced customer support specialist for the SaaS company **{company_name}**.
Your job is to provide clear, concise, and accurate answers to customer inquiries about {product_name}.
Responses should be brief – no more than **150 words** – and include any necessary next‑step actions.

**Context**
Customers ask a variety of questions such as how to use a feature, troubleshoot an error, or request billing information.
You must read the query, identify the main issue, and respond in a friendly tone appropriate for a professional SaaS support environment.

**Step‑by‑Step Instructions**

1. **Read the query** – `{customer_query}`.
2. **Extract the core problem** (e.g., password reset, API error, pricing).
3. **Draft a concise answer** – no more than 150 words.
4. **Add suggested next steps** – at most 3 actions the customer can take.
5. **End with a friendly closing** and a reminder of available support channels (`{support_contact}`).

**Response Format (plain text only)**

```
Answer:
<brief answer, <=150 words>

Suggested Next Steps:
1. <first action>
2. <second action>
3. <third action>

Thank you for choosing {company_name}. If you need further assistance, feel free to contact us at {support_contact}.
```

**Example**

*Input:*
`How can I reset my password?`

*Output:*

```
Answer:
You can reset your password by clicking the "Forgot Password" link on the login page. Enter the email address you used to register, and we’ll send you a secure link to set a new password.

Suggested Next Steps:
1. Go to the login page and click "Forgot Password".
2. Check your inbox (and spam folder) for the reset email.
3. Follow the link and choose a new password that meets our security criteria.

Thank you for choosing {company_name}. If you need further assistance, feel free to contact us at {support_contact}.
```

**Notes**

- Do not include any explanations of the process itself; only provide the answer and next steps.
- Keep the tone helpful, empathetic, and professional.
- Use the placeholders exactly as indicated so the prompt can be customized for different customers or products.

By following these instructions, the AI will consistently deliver short, useful, and brand‑consistent support responses.

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Role and Description

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Using Variables

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Model Selection

• Speed vs Quality - Smaller models are faster but less capable
• Cost - Larger models cost more per token
• Capability - Some tasks require more advanced models
• Latency: Some models use reasoning or thinking tokens, which can add latency to the execution, also see the impact of the Max Iteration and Max Execution Time constraints below.

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Tools

Standard tools are available to your agents.

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Context

1. Click Add context
2. Select a Knowledge Base or Memory Store.

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Attaching Files

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Evaluators and Guardrails

1. Click Add Evaluator or Add Guardrail
2. Select evaluator type
3. Configure evaluation parameters:
   • Define as Input or Output
   • Sample Rate (for Evaluators only)

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Runtime Constraints

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Viewing Tasks (coming soon)

The Tasks will let you visualize task executions for your Agent.

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Agent and Tasks States

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Invoking your Agent

To start a conversation with an agent, we use the Responses API. The following code also needs to be supplied with an API Key from your account (replace <ORQ_API_KEY>. The following codes are available within the AI Studio to use in your implementation. This will invoke the Agent and print its response.

curl --request POST \
  --url 'https://my.orq.ai/v2/agents/demoAgent/responses' \
  --header 'Authorization: Bearer <ORQ_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "message": {
      "role": "user",
      "parts": [
        {
          "kind": "text",
          "text": "Hi! I am testing your capabilities. Can you describe what you can do, the kinds of tasks you are designed to handle, and give an example of how you might help someone in a real-world scenario?"
        }
      ]
    }}'

• role: Who is sending the message ("user" or "assistant")
• parts: Array of message content (supports multimodal: text, images, files)
  • kind: Type of content ("text", "image_url", "file")
  • text: The actual message content

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Agent Traces

• Agent execution history with timestamps
• Input/output for each agent call
• Token usage and costs per execution
• Execution duration and performance metrics
• Errors and debugging information
• Tool calls executed by the agent (function calls, HTTP requests, code execution, or MCP requests)
• Knowledge retrieval results and RAG context used during execution
• Memory store interactions and state management

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Creating Custom Views

1. Set your desired filters
2. Click All Rows (top right)
3. Select Create New View
4. Give your view a title
5. Optionally check Set view private (default is shared with project members)
6. Your filtered view is now saved and accessible from the All Rows dropdown