# Useful but Little-Known Features of ADK 2.0 🌍 What if you could grade your agent on custom criteria that go beyond standard metrics — like compliance, tone, or domain-specific accuracy? ADK 2.0's custom evaluation metrics let you define your own evaluation logic as Python functions, measuring agent quality against any criteria you choose. 📌 Title: Custom Evaluation Metrics 🔗 URL: 🧩 Overview Custom evaluation metrics are Python functions that return an `EvaluationResult`. Each function receives four parameters: `eval_metric` (the metric definition), `actual_invocations` (the agent's actual responses), `expected_invocations` (the expected responses), and `conversation_scenario` (the conversation context). Async functions are fully supported, enabling integration with external services. 🛠 How to use it Create a custom metric function and register it in the eval config JSON. ```python from google.adk.evaluation.eval_metrics import EvalStatus from google.adk.evaluation.evaluator import EvaluationResult async def response_length_metric( eval_metric, actual_invocations, expected_invocations, conversation_scenario ) -> EvaluationResult: # Evaluate whether the response length is appropriate last_response = "".join( part.text for part in actual_invocations[-1].final_response.parts ) score = 1.0 if len(last_response) < 500 else 0.5 return EvaluationResult( overall_score=score, overall_eval_status=( EvalStatus.PASSED if score >= eval_metric.criterion.threshold else EvalStatus.FAILED ), ) ``` Register the metric in eval config JSON with a `criteria` threshold and a `custom_metrics` entry whose `code_config.name` is the function's import path. ```json { "criteria": { "response_length": {"threshold": 0.8} }, "custom_metrics": { "response_length": { "code_config": {"name": "my_metrics.response_length_metric"} } } } ``` 🏗 Building it into production ・Implement domain-specific quality criteria (accuracy, tone, compliance) as metrics ・Run custom metrics automatically in CI/CD pipelines and block deployments when quality drops below thresholds ・Use async metrics to integrate with external evaluation APIs or LLM-as-a-Judge patterns ・Combine multiple metrics to compute a comprehensive quality score for your agent 💡 Use cases 📏 Evaluate response conciseness and information density against custom standards 🏢 Automatically check compliance with industry regulations and requirements 🎯 Verify that specific tool call patterns match expected behavior 🗣 Score response tone and politeness automatically using LLM-as-a-Judge ⚠️ Watch out Heavy processing inside custom metric functions can slow down the entire evaluation run. Use async functions and set timeouts as needed. Also, the `code_config` module path must be importable from the evaluation execution environment. ✨ Custom evaluation metrics let you systematically enforce quality standards that go beyond what built-in metrics can cover. Build evaluations tailored to your specific use case. #ADK# #AIAgent#