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

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.

client.evaluate()

Run an Evaluation and poll until it completes.

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 instead of the baseline
_poll_interval	float	5.0	Seconds between status checks

Returns an Evaluation. 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.

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.

client.adapt()

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

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. After adaptation completes, review and apply the resulting proposals.

client.protect()

Configure Dome runtime Guardrails for an Agent.

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 names
mode	str	"enforce"	"enforce" blocks threats, "monitor" logs only, "disabled" turns Guardrails off

Returns a DomeConfig.

Preview Methods

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.

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

# 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")