# Agent Configuration

 

> **Info:** If you're interested in using this feature, please reach out to your Customer Success Manager or Account Executive.

Your agent dashboard contains everything you need to set up:

* **Agent scope** - If using an accelerator, the default prompt is ready for testing. You can customize it later based on your specific needs.
* **Data sources** - Connect a test data source - e.g. add a test folder from Google Drive with sample documents so your agent has knowledge to reference when answering questions. More information on [Adding a data source](#adding-a-data-source)
* **Tools** - All agents include Knowledge Search Tool by default. Add more tools from templates or create custom ones as your use case requires More information on [Tools](#tools)

## Configuring your agent

When you open an agent project, you'll see the **agent dashboard**.
![agent-dashboard](https://tray.ai/documentation/images/platform/artificial-intelligence/agent-builder/overview/144mcwwAuwR6Npym1JsXRr_agent-dashboard.png)
This is your control center for:
--------------------------------

### **Agent scope**

Agent scope defines what your agent knows and how it behaves. Think of it as the agent's job description and personality guide.
If you're using one of the pre-configured agents accelerators (Knowledge agent, ITSM agent, or Support ticket agent), they come with predefined scope tailored for their specific use cases. This gives you a working starting point that you can customize based on your needs.
Be specific about the role:

```command line
You are a customer support agent for [Company Name], specializing in billing and subscription questions.
```

Define capabilities and limitations:

```json
You can help with: 
- Billing questions and payment issues 
- Subscription upgrades/downgrades 
- Account access problems 

You should escalate to human agents: 
- Technical product issues 
- Refund requests over $100 
- Angry or frustrated customers
```

Set the tone:

```console
Always be professional, helpful, and empathetic. Use clear, non-technical language.
```

#### Best practices for agent scope

**Key prompting tips:**

* **Set behavioral expectations** - Professional tone, structured responses, etc.
* **Define limitations clearly** - What the agent can't do helps users set appropriate expectations
* **Use examples** - Include sample interactions showing desired behavior: "When asked about pricing, respond with: 'I'll look up our current pricing information for you...'"
* **Prioritize tasks** - Tell the agent what to do first: "Always search the knowledge base before providing any factual information"
* **Handle uncertainty** - Specify what to do when information is unclear: "If you're unsure, ask clarifying questions rather than guessing"
* **Set error handling** - Define fallback behaviors: "If a tool fails, explain the issue and suggest alternative approaches"
* **Include persona details** - Describe the agent's personality: "Be friendly but professional, patient with technical questions, and proactive in offering help"
* **Specify output format** - Request structured responses: "For troubleshooting, use numbered steps. For explanations, use clear headings and bullet points"
* **Keep it focused** - While comprehensive, avoid overly complex instructions that might confuse the AI model. Aim for clear, actionable guidance that helps the agent serve your specific use case effectively.

***

### AI model

#### **Tray native model (default)**

* Included with your plan
* Max context window: up to 1 million tokens
* Rate limited
* Best for: Getting started, testing

#### **Bring Your Own Model options**

You can connect your own API keys from third-party AI providers like OpenAI or AWS Bedrock to use their models instead of Tray's default model. Currently supported:

* AWS Bedrock
* OpenAI
* Azure AI
* Google Gemini
  **Setup steps for BYO models**

1. Click **"Change Model"** in the AI Model section
2. Select your provider (OpenAI, Anthropic, etc.)
3. Click **"Create New Authentication"** or select existing
4. Enter your API key from the provider
5. Choose the specific model version
6. Click **"Save"**
   **Advanced settings**

* **Context window (token limit)**: Default is 32k tokens
  * Smaller windows (8k-16k) may be faster but limit conversation memory
  * Standard windows (64k-128k) handle typical conversations and documents well
  * Large windows (256k-500k) support extensive analysis and long conversations
  * Maximum supported: up to 1 million tokens for processing entire books, codebases, or complex multi-document workflows
* **Temperature**: Controls response creativity (0.0 = focused, 1.0 = creative). It controls how predictable versus varied the responses will be - lower values (0.0) make responses more consistent and factual, while higher values (1.0) make responses more varied and creative but potentially less reliable.
  **Recommended defaults**
* **For customer support**: Tray native or GPT-4, temperature 0.3
* **For content creation**: Claude-3-Sonnet, temperature 0.7
* **For data analysis**: GPT-4-turbo, temperature 0.1