Create evaluator

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

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

This evaluator uses an LLM to compare the expected and actual tool trajectories, allowing for nuanced judgment — for example, tolerating minor deviations like extra helper tool calls. It uses the expected_tool_trajectory and actual_tool_trajectory placeholders.

Save the following as trajectory_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 }
      }
    }
  }
}

Create the evaluator:

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

This evaluator checks whether the agent's behavior satisfies a set of assertions, returning a categorical PASS/FAIL/INCONCLUSIVE verdict. It uses the assertions placeholder along with context and available_tools.

Save the following as assertion_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 }
      }
    }
  }
}

Create the evaluator:

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

This evaluator compares the agent's actual response against an expected response, scoring semantic similarity. It uses the expected_response placeholder to receive the ground truth at evaluation time.

Save the following as response_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 }
      }
    }
  }
}

Create the evaluator:

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