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

# Lifecycle Methods

> High-level Vijil client methods that drive the Agent trust lifecycle: evaluate, test, adapt, and protect.

Lifecycle methods are high-level workflows on the `Vijil` client. Each maps to a stage of the Agent trust lifecycle and, where an operation runs asynchronously, polls until it completes by default.

| Method              | What It Does                                      | Returns      |
| ------------------- | ------------------------------------------------- | ------------ |
| `client.evaluate()` | Measure an Agent against known standards          | `Evaluation` |
| `client.test()`     | Explore for weaknesses under adversarial pressure | `Job`        |
| `client.adapt()`    | Improve an Agent through corrective evolution     | `Job`        |
| `client.protect()`  | Configure Dome runtime Guardrails                 | `DomeConfig` |

<Tip>
  `evaluate` measures against known standards, like a certification exam. `test` explores for unknown weaknesses through adversarial pressure, like a penetration test. Both produce Trust Scores.
</Tip>

## `client.evaluate()`

Run an Evaluation and poll until it completes.

```python theme={null}
evaluation = client.evaluate(
    "agent-id",
    baseline=True,          # use the standard trust Harnesses
    # harness_id="h-abc",  # OR use a specific Harness (mutually exclusive with baseline)
)

print(evaluation.status)         # "completed"
print(evaluation.trust_score)    # 0.82
print(evaluation.dimensions)     # Dimensions(reliability=0.9, security=0.8, safety=0.75)
```

| Parameter        | Type          | Default | Description                                                                               |
| ---------------- | ------------- | ------- | ----------------------------------------------------------------------------------------- |
| `agent_id`       | `str`         | —       | Agent ID or alias                                                                         |
| `baseline`       | `bool`        | `False` | Run the standard trust Harnesses (reliability, security, safety)                          |
| `harness_id`     | `str \| None` | `None`  | Run a specific [Harness](/concepts/evaluation-components/harness) instead of the baseline |
| `_poll_interval` | `float`       | `5.0`   | Seconds between status checks                                                             |

Returns an [`Evaluation`](/developer-guide/sdk/models-errors). Pass either `baseline=True` or a `harness_id`, not both.

## `client.test()`

Create a Red Swarm test engagement and, by default, poll until it finishes.

```python theme={null}
job = client.test(
    "agent-id",
    mode="adaptive",        # or "comprehensive"
    no_wait=False,          # True to return immediately
)

print(job.status)   # "completed"
print(job.id)       # "job-abc123"
```

| Parameter    | Type                | Default              | Description                                                                           |
| ------------ | ------------------- | -------------------- | ------------------------------------------------------------------------------------- |
| `agent_id`   | `str`               | —                    | Agent ID or alias                                                                     |
| `mode`       | `str`               | `"adaptive"`         | `"adaptive"` adjusts strategy from responses; `"comprehensive"` sweeps all categories |
| `no_wait`    | `bool`              | `False`              | Return immediately without polling                                                    |
| `tool`       | `str`               | `"diamond_security"` | Red-team tool to use                                                                  |
| `purpose`    | `str`               | `""`                 | Description of the Agent's purpose                                                    |
| `categories` | `list[str] \| None` | `None`               | Test categories, for example `["injection"]`                                          |

Returns a [`Job`](/developer-guide/sdk/models-errors).

## `client.adapt()`

Create a corrective evolution job that improves an existing Agent based on weaknesses found by evaluation or testing.

```python theme={null}
job = client.adapt("agent-id", mode="config")
print(job.status)   # "completed"
```

| Parameter  | Type   | Default    | Description                                   |
| ---------- | ------ | ---------- | --------------------------------------------- |
| `agent_id` | `str`  | —          | Agent ID or alias                             |
| `mode`     | `str`  | `"config"` | `"prompt"`, `"config"`, `"code"`, or `"dome"` |
| `no_wait`  | `bool` | `False`    | Return immediately without polling            |

Returns a [`Job`](/developer-guide/sdk/models-errors). After adaptation completes, review and apply the resulting [proposals](/developer-guide/sdk/resources#client-proposals).

## `client.protect()`

Configure [Dome](/developer-guide/protect/overview) runtime Guardrails for an Agent.

```python theme={null}
dome = client.protect(
    "agent-id",
    guards=["prompt_injection", "pii"],
    mode="enforce",
)

print(dome.mode)     # "enforce"
print(dome.guards)   # ["prompt_injection", "pii"]
```

| Parameter   | Type                | Default     | Description                                                                          |
| ----------- | ------------------- | ----------- | ------------------------------------------------------------------------------------ |
| `agent_id`  | `str`               | —           | Agent ID or alias                                                                    |
| `policy_id` | `str \| None`       | `None`      | Policy to apply                                                                      |
| `guards`    | `list[str] \| None` | `None`      | [Guard](/concepts/defense/guard) names                                               |
| `mode`      | `str`               | `"enforce"` | `"enforce"` blocks threats, `"monitor"` logs only, `"disabled"` turns Guardrails off |

Returns a [`DomeConfig`](/developer-guide/sdk/models-errors).

## Preview Methods

<Info>
  The following methods target Console routes that are not yet generally available. Their signatures are stable, but calls fail until the corresponding Console capability ships.
</Info>

| Method              | What It Does                                               | Returns                 |
| ------------------- | ---------------------------------------------------------- | ----------------------- |
| `client.discover()` | Find Agents in GitHub repositories or cloud infrastructure | `Page[DiscoveredAgent]` |
| `client.register()` | Convert Agent source into an A2A card and genome           | `dict`                  |
| `client.evolve()`   | Create a new Agent through generative evolution            | `Job`                   |
| `client.deploy()`   | Deploy an Agent to a production runtime                    | `Deployment`            |

```python theme={null}
# Discover Agents in a GitHub organization or a Kubernetes namespace
agents = client.discover(github_org="acme-corp")
agents = client.discover(provider="k8s", namespace="production")

# Register an Agent from a GitHub URL or a local directory
result = client.register("https://github.com/acme/my-agent")

# Create a new Agent from a spec, a file, or a natural-language description
job = client.evolve(description="A travel booking agent for flights and hotels")

# Deploy an Agent to a runtime
deployment = client.deploy("agent-id", runtime="agentcore")
```