> For the complete documentation index, see [llms.txt](https://angoor-ai.gitbook.io/angoor-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://angoor-ai.gitbook.io/angoor-ai/basics/configuration/agents.md).
# Agents
The Agents section enables you to create AI-powered conversational assistants that automate customer interactions, answer inquiries, and execute specific tasks tailored to your business needs.
## Agents Dashboard
Your central hub for managing all AI agents displays comprehensive configuration and performance metrics:
* **Agent Name:** Unique identifier for your AI assistant (e.g., "Customer Support Bot", "Lead Qualifier")
* **Creation Details:** Timestamp and owner information for tracking and accountability
* **Variables:** Count of dynamic placeholders for personalized interactions
* **Knowledge Base:** Connected information sources powering accurate responses
* **Description:** Purpose and capabilities summary for team reference
* **Tags:** Organizational labels for filtering and categorization
## Creating Your First Agent
**Step 1:** Click "Create Agent" and enter a descriptive name that clearly indicates the agent's purpose.
**Step 2:** Define your agent's personality and behavior in the Core tab using structured prompts and instructions.
**Step 3:** Configure technical settings including AI model selection, response parameters, and custom features.
**Step 4:** Connect knowledge bases and forms to provide context and enable data collection capabilities.
**Step 5**: Test your agent with sample conversations before deploying to production.
## Core Tabs
The Core tab is your workspace for crafting agent personality, knowledge integration, and conversation logic.
### **Prompt Engineering**
Create comprehensive instructions using a structured template approach:
```
## Job Description
Define what your agent does and its primary responsibilities
## Character
Establish personality traits and communication style
## Information to Collect
Specify data points to gather during conversations
## Tone and Personality
Set the emotional intelligence and interaction approach
## Style and Language
Define vocabulary, formality level, and cultural considerations
```
For comprehensive guidance on advanced prompting techniques and best practices, refer to these detailed documentation resources: [OpenAI Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide) & [Anthropic Claude 4 Best Practices](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/claude-4-best-practices).
### **Dynamic Variables**
Add personalization elements that adapt to each conversation:
* Click "Add Variable" to create placeholders for user data
* Reference variables in prompts using {{ }}, {{ }} format or by reference variable button.
* You populate these variables at any point in the flow
### **Knowledge Reference**
Enhance agent intelligence with organizational information:
* Use "Refer Knowledge Base" to connect documentation, FAQs, and product information
* Reference specific knowledge sections within prompts for contextual responses
* Combine multiple knowledge sources for comprehensive coverage
### **Conditional Logic**
Create intelligent response paths with flexible conditional logic:
* Add conditions using Field-Operator-Value structure
* Design different conversation flows for various scenarios
* Stack multiple conditions for complex decision trees
## Settings Tabs
#### Metadata
Configure tool name, description, and tags for clear identification and organization across your workspace.
#### Model Selection
Choose from GPT 4.1, GPT 3.5, or custom models with token capacities of 8K, 16K, 32K, or 100K and Standard or Fast response times. More models will be available soon
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://angoor-ai.gitbook.io/angoor-ai/basics/configuration/agents.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.