Crear evaluador

agentcore add evaluator \
  --name "your_custom_evaluator_name" \
  --config custom_evaluator_config.json \
  --level "TRACE"

Este comando añade el evaluador a la configuración localagentcore.json. Ejecute agentcore deploy para crearlo en su AWS cuenta.

Introduzca un nombre para su evaluador personalizado.

Seleccione el nivel de evaluación: Session, Trace o Tool Call.

Elija el modelo de juez LLM para la evaluación.

Introduzca sus instrucciones de evaluación. El mensaje debe incluir al menos un marcador de posición: {context} para el historial de conversaciones o {available_tools} para la lista de herramientas.

Seleccione una escala de valoración preestablecida o defina una escala personalizada.

Revise la configuración del evaluador y pulse Entrar para confirmar.

import json
from bedrock_agentcore_starter_toolkit import Evaluation

eval_client = Evaluation()

# Load the configuration JSON file
with open('custom_evaluator_config.json') as f:
    evaluator_config = json.load(f)

# Create the custom evaluator
custom_evaluator = eval_client.create_evaluator(
    name="your_custom_evaluator_name",
    level="TRACE",
    description="Response quality evaluator",
    config=evaluator_config
)

import boto3
import json

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

# Load the configuration JSON file
with open('custom_evaluator_config.json') as f:
    evaluator_config = json.load(f)

# Create the custom evaluator
response = client.create_evaluator(
    evaluatorName="your_custom_evaluator_name",
    level="TRACE",
    evaluatorConfig=evaluator_config
)

aws bedrock-agentcore-control create-evaluator \
    --evaluator-name 'your_custom_evaluator_name' \
    --level TRACE \
    --evaluator-config file://custom_evaluator_config.json

Este evaluador utiliza un LLM para comparar las trayectorias esperadas y reales de las herramientas, lo que permite realizar juicios matizados; por ejemplo, tolera pequeñas desviaciones, como utilizar herramientas auxiliares adicionales. Utiliza los marcadores de posición y. expected_tool_trajectory actual_tool_trajectory

Guarde lo siguiente comotrajectory_compliance_config.json:

{
  "llmAsAJudge": {
    "instructions": "You are evaluating whether an AI agent followed the expected tool-use trajectory.\n\nExpected trajectory (ordered list of tool names):\n{expected_tool_trajectory}\n\nActual trajectory (ordered list of tool names the agent used):\n{actual_tool_trajectory}\n\nFull session context:\n{context}\n\nAvailable tools:\n{available_tools}\n\nCompare the expected and actual trajectories. Consider whether the agent called the right tools in the right order. Minor deviations (e.g., an extra logging tool call) are acceptable if the core trajectory is preserved.",
    "ratingScale": {
      "numerical": [
        { "label": "No Match",      "value": 0.0, "definition": "The actual trajectory has no meaningful overlap with the expected trajectory" },
        { "label": "Partial Match", "value": 0.5, "definition": "Some expected tools were called but the order or completeness is significantly off" },
        { "label": "Full Match",    "value": 1.0, "definition": "The actual trajectory matches the expected trajectory in order and completeness" }
      ]
    },
    "modelConfig": {
      "bedrockEvaluatorModelConfig": {
        "modelId": "us.anthropic.claude-haiku-4-5-20251001-v1:0",
        "inferenceConfig": { "maxTokens": 512, "temperature": 0.0 }
      }
    }
  }
}

Cree el evaluador:

aws bedrock-agentcore-control create-evaluator \
  --evaluator-name 'TrajectoryCompliance' \
  --level SESSION \
  --description 'Evaluates whether the agent followed the expected tool trajectory.' \
  --evaluator-config file://trajectory_compliance_config.json

Este evaluador comprueba si el comportamiento del agente satisface un conjunto de afirmaciones y emite un veredicto categórico. PASS/FAIL/INCONCLUSIVE Utiliza el assertions marcador de posición junto con y. context available_tools

Guarde lo siguiente comoassertion_checker_config.json:

{
  "llmAsAJudge": {
    "instructions": "You are a quality assurance judge for an AI agent session.\n\nSession context (full conversation history):\n{context}\n\nAvailable tools:\n{available_tools}\n\nAssertions to verify:\n{assertions}\n\nFor each assertion, determine if the session satisfies it. The overall verdict should be PASS only if ALL assertions are satisfied. If any assertion fails, the verdict is FAIL. If the session data is insufficient to determine, verdict is INCONCLUSIVE.",
    "ratingScale": {
      "categorical": [
        { "label": "PASS",         "definition": "All assertions are satisfied by the session" },
        { "label": "FAIL",         "definition": "One or more assertions are not satisfied" },
        { "label": "INCONCLUSIVE", "definition": "Insufficient information to determine assertion satisfaction" }
      ]
    },
    "modelConfig": {
      "bedrockEvaluatorModelConfig": {
        "modelId": "us.anthropic.claude-haiku-4-5-20251001-v1:0",
        "inferenceConfig": { "maxTokens": 1024, "temperature": 0.0 }
      }
    }
  }
}

Cree el evaluador:

aws bedrock-agentcore-control create-evaluator \
  --evaluator-name 'AssertionChecker' \
  --level SESSION \
  --description 'Checks whether the agent session satisfies a set of assertions.' \
  --evaluator-config file://assertion_checker_config.json

Este evaluador compara la respuesta real del agente con una respuesta esperada y puntúa la similitud semántica. Utiliza el expected_response marcador de posición para recibir la verdad fundamental en el momento de la evaluación.

Guarde lo siguiente comoresponse_similarity_config.json:

{
  "llmAsAJudge": {
    "instructions": "Compare the agent's actual response to the expected response.\n\nConversation context:\n{context}\n\nAgent's actual response:\n{assistant_turn}\n\nExpected response:\n{expected_response}\n\nEvaluate semantic similarity. The agent does not need to match word-for-word, but the meaning, key facts, and intent should align. Penalize missing critical information or contradictions.",
    "ratingScale": {
      "numerical": [
        { "label": "No Match",        "value": 0.0,  "definition": "The response contradicts or is completely unrelated to the expected response" },
        { "label": "Low Similarity",   "value": 0.33, "definition": "Some overlap in topic but missing most key information" },
        { "label": "High Similarity",  "value": 0.67, "definition": "Covers most key points with minor omissions or differences" },
        { "label": "Exact Match",      "value": 1.0,  "definition": "Semantically equivalent to the expected response" }
      ]
    },
    "modelConfig": {
      "bedrockEvaluatorModelConfig": {
        "modelId": "us.anthropic.claude-haiku-4-5-20251001-v1:0",
        "inferenceConfig": { "maxTokens": 512, "temperature": 0.0 }
      }
    }
  }
}

Cree el evaluador:

aws bedrock-agentcore-control create-evaluator \
  --evaluator-name 'ResponseSimilarity' \
  --level TRACE \
  --description 'Evaluates how closely the agent response matches the expected response.' \
  --evaluator-config file://response_similarity_config.json