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

# Tracking Agents

> Use the `@agent` decorator to create agent spans

All operations are automatically associated with the agent that originated them. Agents are given a name which is what you will see in the dashboard.

<Frame type="glass" caption="This operation is labeled with the name of the Agent that originated it">
  <img height="200" src="https://mintcdn.com/agentops/FtF2hCpmQ3f-_c4T/images/agent-name.png?fit=max&auto=format&n=FtF2hCpmQ3f-_c4T&q=85&s=5ec26ca53f7cde1f5cf0a8bf5b3ec8e8" data-path="images/agent-name.png" />
</Frame>

The example below creates an agent class with a custom name:

```python theme={null}
from agentops.sdk.decorators import agent

@agent(name='ResearchAgent')
class MyAgent:
    def __init__(self):
        # Agent initialization
        pass
        
    # Agent methods
```

If you don't specify a name, the agent will use the class name by default:

```python theme={null}
@agent
class ResearchAgent:
    # This agent will have the name "ResearchAgent"
    pass
```

## Nesting Operations Under Agents

Operations performed by an agent should be decorated with the `@operation` decorator to ensure they're properly nested under the agent:

```python theme={null}
from agentops.sdk.decorators import agent, operation

@agent
class ResearchAgent:
    @operation
    def search_web(self, query):
        # Search implementation
        return results
        
    @operation
    def analyze_data(self, data):
        # Analysis implementation
        return analysis
```

## Session Context

Agents should be created within a session context to ensure proper tracing:

```python theme={null}
from agentops.sdk.decorators import session, agent, operation

@agent
class ResearchAgent:
    @operation
    def perform_research(self, topic):
        # Research implementation
        return results

@session
def research_workflow(topic):
    agent = ResearchAgent()
    return agent.perform_research(topic)
    
# Run the session
result = research_workflow("quantum computing")
```