> ## Documentation Index
> Fetch the complete documentation index at: https://penseapp.vercel.app/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Agents
> Create AI agents to evaluate and improve
An agent is a core building block of Calibrate. Agents can:
* Respond intelligently using LLM models following the instructions given to them
* Use tools for structured data extraction
* Process voice conversations using Speech To Text (STT) and Text To Speech (TTS)
Already have a deployed agent? You can connect it to Calibrate via its HTTP
endpoint instead of rebuilding it here. See [Agent
Connections](/docs/core-concepts/agent-connections) for the setup guide.
## Quickstart
### Navigate to agents
From the sidebar, click **Agents** to view your existing agents or create a new one.
### Create a new agent
Click the **New agent** button in the top right corner.
Select **Build your agent in Calibrate**, give it a name, and click Create.
Once ready, you'll be taken to the agent configuration page with multiple tabs.
## Configure your agent
### Add instructions and select providers
Configure the core agent behavior.
**System prompt**
Define the context for the agent, its persona, how it should behave, and any guidelines it should follow. Write clear instructions.
Be specific about the agent's tone, capabilities, and limitations in the
system prompt.
For detailed guidance on writing effective system prompts, see [ElevenLabs Prompting Guide](https://elevenlabs.io/docs/agents-platform/best-practices/prompting-guide) and [OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering).
**Speech To Text**
Select the STT provider for the agent.
**Text To Speech**
Select the TTS provider for the agent.
**LLM**
Select the provider and model for the agent.
Click **Save** in the top right to save your configuration.
### Add tools
Add tools to enable structured outputs by the agent.
Click **Add tool** to attach tools to your agent. See the [Tools guide](/docs/core-concepts/tools) for how to create tools.
**In-built tools**
The platform provides built-in tools like "End conversation" that are automatically available to all agents.
### Define data extraction fields
Define fields for extracting structured data from conversations.
Click **Add field** to define custom data specifications to extract from conversation transcripts.
### Add tests
Add tests to evaluate your agent's performance.
* Click **Add test** to attach tests to your agent
* Click **Run all tests** to execute all tests for this agent
* Click **Compare models** to benchmark different LLMs
See the [LLM Testing guide](/docs/quickstart/text-to-text) for how to create tests.
### Configure advanced settings
Configure advanced agent behavior.
**Agent speaks first**
Toggle whether the agent should initiate the conversation.
**Max assistant turns**
Set the maximum number of assistant turns before ending the call.
Click **Save** in the top right to save your configuration.
## Next steps
Connect your existing deployed agent to Calibrate
Test your agent's responses and tool usage
Simulate conversations with the agent to identify issues before deployment
Define tools for structured data extraction