Crea una valutazione online

# Create online evaluation configuration
agentcore add online-eval \
  --name "your_config_name" \
  --runtime "your_runtime_name" \
  --evaluator "Builtin.GoalSuccessRate" "Builtin.Helpfulness" \
  --sampling-rate 1.0 \
  --enable-on-create

Questo comando aggiunge la configurazione di valutazione online al tuo localeagentcore.json. Esegui agentcore deploy per crearlo nel tuo AWS account.

Inserisci un nome per la configurazione di valutazione online.

Seleziona i valutatori da includere. Puoi scegliere tra valutatori integrati e qualsiasi valutatore personalizzato che hai creato.

Imposta la frequenza di campionamento, ovvero la percentuale di richieste degli agenti che verranno valutate.

Scegli se abilitare la valutazione automaticamente dopo l'implementazione.

Rivedi la configurazione e premi Invio per confermare.

from bedrock_agentcore_starter_toolkit import Evaluation

# Initialize the evaluation client
eval_client = Evaluation()

# Replace these with your actual values
config_name = "YOUR_CONFIG_NAME"
agent_id = "YOUR_AGENT_ID"  # e.g., agent_myagent-ABC123xyz

# Create online evaluation configuration
config = eval_client.create_online_config(
    config_name=config_name,                  # Must use underscores, not hyphens
    agent_id=agent_id,                        # Agent ID (e.g., agent_myagent-ABC123xyz)
    sampling_rate=1.0,                        # Percentage to evaluate (0-100). 1.0 = evaluate 1% of interactions
    evaluator_list=["Builtin.GoalSuccessRate", "Builtin.Helpfulness"],  # List of evaluator IDs
    config_description="Online Evaluation Config",  # Optional description
    auto_create_execution_role=True,          # Automatically creates IAM role (default: True)
    enable_on_create=True                     # Enable immediately after creation (default: True)
)

print("✅ Online evaluation configuration created!")
print(f"Config ID: {config['onlineEvaluationConfigId']}")
print(f"Status: {config['status']}")

# Save the config ID for later operations
config_id = config['onlineEvaluationConfigId']
print(f"\nSaved config_id: {config_id}")

import boto3

# Your input log group that contains agent traces
LOG_GROUP_NAME = "/aws/agentcore/test-agent-traces"

# The service.name attribute from Otel
SERVICE_NAME = "strands_healthcare_single_agent.DEFAULT"

# The role created earlier with the required permissions for eval
role_arn = f"arn:aws:iam::{ACCOUNT_ID}:role/AgentCoreEvaluationRole"

client = boto3.client('bedrock-agentcore-control')

create_config_response = client.create_online_evaluation_config(
    onlineEvaluationConfigName="strands_healthcare_agent_1",
    description="Continuous evaluation of a healthcare agent",
    rule={
        "samplingConfig": {"samplingPercentage": 80.0}
    },
    dataSourceConfig={
        "cloudWatchLogs": {
            "logGroupNames": [LOG_GROUP_NAME],
            "serviceNames": [SERVICE_NAME]
        }
    },
    evaluators=[{"evaluatorId":"Builtin.Helpfulness"}],
    evaluationExecutionRoleArn=role_arn,
    enableOnCreate=True
)

aws bedrock-agentcore-control create-online-evaluation-config \
    --online-evaluation-config-name "strands_healthcare_agent_1" \
    --description "Continuous evaluation of a healthcare agent" \
    --rule '{"samplingConfig": {"samplingPercentage": 80.0}}' \
    --data-source-config '{"cloudWatchLogs": {"logGroupNames": ["/aws/agentcore/test-agent-traces"], "serviceNames": ["strands_healthcare_single_agent.DEFAULT"]}}' \
    --evaluators '[{"evaluatorId": "Builtin.Helpfulness"}]' \
    --evaluation-execution-role-arn "arn:aws:iam::{YOUR_ACCOUNT_ID}:role/AgentCoreEvaluationRole" \
    --enable-on-create