> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kubiya.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Agents & Teams

> Create and manage AI agents and multi-agent teams

## Overview

Agents are the core execution units in Kubiya. They can operate individually or collaborate as teams to accomplish complex tasks. This section covers creating, configuring, and executing agents.

## Agents

Individual AI agents that can execute tasks, interact with tools, and respond to prompts. Each agent has:

* A configured runtime environment
* Access to specific tools and integrations
* Associated policies for governance
* LLM model configuration

**Key capabilities:**

* Execute tasks synchronously or asynchronously
* Stream execution results in real-time
* Access context from projects and environments
* Use skills and integrations

## Teams

Multi-agent teams coordinate multiple specialized agents to handle complex workflows. Teams enable:

* Agent specialization and role assignment
* Parallel task execution
* Collaborative problem-solving
* Shared context and knowledge

## Task Planning

AI-powered analysis and planning for complex tasks. The task planner:

* Breaks down complex requests into steps
* Identifies required tools and resources
* Suggests optimal execution strategies
* Estimates task complexity

## Common Workflows

### Creating an Agent

```bash theme={null}
POST /api/v1/agents
{
  "name": "DevOps Assistant",
  "description": "Helps with infrastructure and deployment tasks",
  "runtime": "claude_code",
  "model_id": "anthropic/claude-sonnet-4-5",
  "system_prompt": "You are a DevOps expert. Help with Kubernetes, cloud infrastructure, and deployments.",
  "skill_ids": ["skill-uuid-1", "skill-uuid-2"],
  "environment_ids": ["env-uuid-1"]
}
```

**Required fields**: `name`
**Note**: Use `model_id` (not `model`) and `runtime` should be `"claude_code"` or `"default"` (Agno)

### Executing a Task

```bash theme={null}
POST /api/v1/agents/{agent_id}/execute
{
  "prompt": "Check the status of pods in the production namespace",
  "worker_queue_id": "worker-queue-uuid",
  "stream": true
}
```

**Required fields**: `prompt`, `worker_queue_id`
**Note**: `worker_queue_id` must be a valid UUID of an existing worker queue

### Creating a Team

```bash theme={null}
POST /api/v1/teams
{
  "name": "Incident Response Team",
  "description": "Multi-agent team for handling production incidents",
  "configuration": {
    "instructions": "Coordinate to investigate and resolve incidents quickly",
    "reasoning": true
  },
  "skill_ids": ["skill-uuid-1", "skill-uuid-2"]
}
```

**Required fields**: `name`
**Note**: Teams use the Agno framework for multi-agent coordination

### Executing a Team Task

```bash theme={null}
POST /api/v1/teams/{team_id}/execute
{
  "prompt": "Investigate the recent spike in API errors",
  "worker_queue_id": "worker-queue-uuid",
  "stream": true
}
```

## Best Practices

1. **Agent Design**: Create focused agents with specific responsibilities
2. **Team Composition**: Use 2-4 agents per team for optimal coordination
3. **Task Streaming**: Enable streaming for long-running tasks
4. **Context Management**: Provide relevant context through projects and environments

## Next Steps

Explore the API endpoints to learn how to create, configure, and execute agents and teams.