# Agentic Prompts

**Karini AI’s Agent**  offers a comprehensive suite of capabilities to build advanced generative AI applications in complex production environments. 

Using **Karini AI’s prompt playground**, you can create AI agents that integrate the reasoning capabilities of large language models (LLMs) with the ability to take actionable steps, creating a more sophisticated system that can understand and process information, evaluate situations, take appropriate actions, communicate responses, and track ongoing situations.

In the prompt playground, you can create an agent prompt by selecting the task type as **Agent**. 

 **Key components of an Agent are:**

* **LLM (Large Language Model):** LLM is responsible for generating traces, reasoning and actions for the task.
* **Agent prompt:** Agent prompt helps LLM in generating both reasoning traces and actions. Agent prompt may include variables, instructions for request handling and response processing. 
* **Tools:** Tools enable the agent to interact with external systems to retrieve information and perform actions. In Karini AI, the **Tools** tab is organized into two sections:
  * [**Agent Tools**](/karini-ai-documentation/prompt-management/agentic-prompts/create-agent-prompt.md#configuring-agent-tools): Tools configured directly for the agent (for example, internal connectors such as Catalog or Database). These tools are available for use during agent execution.
  * [**MCP Server**](/karini-ai-documentation/prompt-management/agentic-prompts/create-agent-prompt.md#configuring-mcp-server): Tools provided through an MCP (Model Context Protocol) server. Connecting an MCP server allows you to expose and use externally hosted tools within the agent.
* Karini AI's agent prompt supports the following Agent tools:
  * Agent
  * Catalog
  * Database
  * Knowledgebase
  * Dataset (Vector Store)
  * Prompt (LLM)
  * REST API
  * KnowledgeGraph
  * Lambda
  * Amazon Q Retriever
  * Amazon Q Accessor Retriever
  * Amazon Bedrock Knowledge Base
  * Messaging
  * Browser Use
  * SAP Odata
  * Salesforce
  * Code Interpreter

Refer to the following video to create and test agent prompt.

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F7ZrVuiAUMyuYVvrK5KaB%2Fuploads%2FDb7fJqGcHsO9Tv3PxBpP%2Fagent_prompt_building.mp4?alt=media&token=d0cfc7fe-a945-412f-a06d-d59c816a8823>" %}

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# Agent Instructions: 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:

```
GET https://karini-ai.gitbook.io/karini-ai-documentation/prompt-management/agentic-prompts.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.