> ## Documentation Index
> Fetch the complete documentation index at: https://docs.seekr.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Agents
> Configurable AI systems that reason through problems and execute tasks autonomously.
export const SupportedOn = ({ui = false, api = true, sdk = true}) => ;
An agent is an AI system that reasons through problems and executes tasks autonomously. Agents are configured by specifying models, tools, instructions, and reasoning approach—providing everything needed to accomplish tasks from simple workflows to complex, open-ended objectives.
Create and manage agents through the SeekrFlow web interface.
Create and manage agents programmatically with the Python SDK.
## Agent primitives
Agents are built from two core primitives:
A large language model (LLM) serves as the agent's cognitive engine, providing the reasoning capabilities needed to understand tasks, determine the best course of action, and generate responses. SeekrFlow supports both base models and fine-tuned models.
Tools are modular components that extend an agent's capabilities beyond built-in reasoning. With tools, agents can take actions, access external systems, or query knowledge stores.
## Agent configuration
When creating an agent, you configure several key properties:
| Property | Description |
| ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Name** | Identifier for the agent |
| **Instructions** | System prompt that guides how the agent plans its actions and generates its response (also called developer message) |
| **Model** | Which LLM to use for generating responses (can be a base model or fine-tuned model) |
| **Tools** | Which tools the agent can access |
| **Reasoning effort** | Controls how much reasoning the agent uses for planning |
| **Temperature** *(optional)* | Controls how predictable or varied the agent's planning is. Lower values produce more consistent plans. Higher values introduce more variation. Default is `0.6`. |
## Reasoning modes
| Level | Description |
| ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Speed-optimized** | Streamlined reasoning for low-latency applications. Best for scenarios requiring quick responses with a limited set of tools. |
| **Performance-optimized** | Enhanced reasoning for complex scenarios. Best for agents managing multiple tools or executing sophisticated workflows where accuracy outweighs response speed. |
## Agent status
Each agent has a status that indicates its deployment state:
| Status | Description |
| ------------ | ------------------------------------------------------------------------------------------------------------------------ |
| **Active** | Ready to serve requests |
| **Pending** | Transitioning between states. Either recently promoted and moving to active, or recently demoted and moving to inactive. |
| **Updating** | An update is in progress. Returns to active once changes take effect. |
| **Inactive** | Not currently deployed, requires activation before serving requests |
| **Failed** | Deployment failed. The agent cannot serve requests. To recover, address the issue and promote the agent manually. |
## Multi-agent workflows
Agents can delegate subtasks to other agents using the agent-as-tool pattern. A supervisor agent remains in control throughout a run, invoking sub-agents as callable tools to handle specialized tasks and then integrating their results into a unified response. This enables composable, multi-agent systems where each agent focuses on a specific capability. For implementation details, see [Agent as tool](/flow/sdk/agents/agent-as-tool).