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

# EvaluationSubject Fields

> Describe the agent being evaluated so analysis can check instruction adherence

`subject` (passed to `client.evaluations.run()`) tells AgentX what it's evaluating. It's used by AI analysis to check instruction adherence and to power the Sovereignty & Portability matrix; it never changes how scoring itself runs.

```python theme={null}
report = (
    client.evaluations
    .run(
        dataset_id="...",
        subject={
            "kind": "custom_agent",
            "displayName": "Support Bot",
            "framework": "langchain",
            "runtime": "local",
            "agentInstructions": "You are a helpful support agent...",
        },
    )
    .execute(my_agent)
    .finalize()
    .analyze()
)
```

## Fields

| Field               | Values                                                                                                                                                             | Description                                                                            |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- |
| `kind`              | `"custom_agent"`                                                                                                                                                   | Always `"custom_agent"` for externally-owned agents evaluated via this SDK             |
| `displayName`       | `str`                                                                                                                                                              | Human-readable identifier shown in the dashboard                                       |
| `framework`         | `"raw_python"` \| `"openai"` \| `"anthropic"` \| `"google"` \| `"langchain"` \| `"llamaindex"` \| `"crewai"` \| `"autogen"` \| `"n8n"` \| `"flowise"` \| `"other"` | Underlying technology stack                                                            |
| `runtime`           | `"local"` \| `"ci"` \| `"customer_hosted"` \| `"low_code"`                                                                                                         | Where the agent is running                                                             |
| `version`           | `str`                                                                                                                                                              | Optional version identifier                                                            |
| `endpoint`          | `str`                                                                                                                                                              | Optional URL, for HTTP-based agents                                                    |
| `agentInstructions` | `str`                                                                                                                                                              | Optional system prompt / instructions, checked against actual behavior during analysis |

`framework` and `runtime` are the only fields required beyond `kind` and `displayName`. Set them to `"other"` and `"local"` if nothing else fits.

## Return values from your agent function

Whatever callable you pass to `.execute()` can return:

* **A string**: used directly as the output text.
* **A dict with an `"output"` key**: the output text is extracted; a `"trace_id"` key links the result to a recorded trace (enabling View trace, trajectory-aware judging, and [expected-trajectory matching](/sdk/evaluations/build-dataset#expected-trajectories)); a `"retrieval_context"` key (string or chunk list) feeds [RAG judging](/evaluation/rag); everything else in the dict is retained as metadata.
* **An `EvaluationResult`**: full control over output, metadata, observable trace, and timing:

```python theme={null}
from agentx.evaluations.models import EvaluationResult

def my_agent(case):
    return EvaluationResult(
        output="Click Forgot Password on the login screen.",
        metadata={"model": "gpt-4o"},
        latency_ms=1240,
        input_tokens=150,
        output_tokens=45,
    )
```

<Note>
  Sensitive values are redacted automatically before anything is uploaded: API keys (`sk-...`), bearer tokens, authorization headers, and password-like fields. Raw prompts are never uploaded; only the response text and metadata you explicitly include are sent.
</Note>