AI Agents with LangChain & Autonomous Workflows · レッスン

エージェントのパフォーマンス評価

AIエージェントの有効性と信頼性を定量的に評価する手法や指標を学びます。

「エージェントのパフォーマンス評価」はCoddyKit上の無料AI Agents with LangChain & Autonomous Workflowsレッスンです。これはレッスン3/4です。下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。これはAI Agents with LangChain & Autonomous Workflows学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Why Evaluate Your Agent?

Building an AI agent is exciting, but how do you know if it's actually performing well? That's where evaluation comes in!

Agent evaluation is the process of assessing your agent's performance, reliability, and effectiveness. It helps you understand if your agent is doing what you designed it to do, and where it might need improvement.

What Metrics Matter?

When evaluating agents, we look at several key metrics. These help us quantify different aspects of performance:

Accuracy: Does the agent provide correct answers or actions?
Latency: How quickly does the agent respond?
Cost: How much does it cost to run the agent (e.g., API calls)?
Robustness: How well does it handle unexpected or varied inputs?

Is the Answer Correct?

Accuracy is often the first thing people think about. It measures how often your agent produces the correct or desired output.

For a Q&A agent, accuracy means giving the right answer. For a task-oriented agent, it means successfully completing the task as intended.

Defining "correct" can sometimes be tricky and might require human judgment, especially for subjective tasks.

Speed and Expense

Latency refers to the time it takes for your agent to process an input and generate a response. A slow agent can frustrate users!

Cost is another critical factor. Every API call to an LLM or external tool incurs a cost. Optimizing your agent for lower costs is essential for production deployments.

Balancing speed and cost with accuracy is a common challenge in agent development.

Agent Resilience

An agent's robustness measures its ability to perform consistently across a wide range of inputs, including those that are ambiguous, malformed, or unexpected.

A robust agent won't easily "break" or give nonsensical answers when faced with slight variations or tricky edge cases. Testing for robustness involves trying diverse scenarios.

The Ground Truth

To evaluate an agent quantitatively, you need a set of test cases with known, correct answers. This is called your evaluation set or ground truth data.

Your evaluation set should:

Contain diverse inputs that reflect real-world usage.
Have clearly defined expected outputs for each input.
Be separate from any data used to train or develop the agent.

Human vs. Machine Review

Agent evaluation can be done in two main ways:

Manual Evaluation: Humans review agent outputs and judge their quality, correctness, and relevance. This is crucial for subjective tasks.
Automated Evaluation: Programs compare agent outputs to a predefined "ground truth" using metrics like accuracy. This is faster and scalable for objective tasks.

Often, a combination of both approaches yields the best results.

Putting it to the Test

Let's look at a very simplified Python example that simulates evaluating an agent's responses against expected answers. This demonstrates the core idea of programmatic checking.

Try running this example:

def evaluate_response(question, agent_output, expected_output):
    print(f"Q: {question}")
    print(f"Agent Output: {agent_output}")
    print(f"Expected Output: {expected_output}")
    is_correct = (agent_output.strip().lower() == expected_output.strip().lower())
    print(f"Correct? {is_correct}\n")
    return is_correct

# Simulate agent responses for a few questions
test_cases = [
    {"q": "What is 10 + 5?", "agent": "15", "expected": "15"},
    {"q": "Capital of France?", "agent": "Paris", "expected": "Paris"},
    {"q": "Who invented the lightbulb?", "agent": "Edison", "expected": "Nikola Tesla"}, # Intentionally incorrect
    {"q": "Tell me a fun fact.", "agent": "The shortest war in history...", "expected": "The shortest war in history..."}
]

correct_count = 0
for case in test_cases:
    if evaluate_response(case["q"], case["agent"], case["expected"]):
        correct_count += 1

accuracy = (correct_count / len(test_cases)) * 100
print(f"--- Evaluation Summary ---")
print(f"Total Questions: {len(test_cases)}")
print(f"Correct Answers: {correct_count}")
print(f"Accuracy: {accuracy:.2f}%")

Making Sense of Scores

Once you run your evaluation, you'll get scores for your chosen metrics. These numbers aren't just for show – they guide your next steps!

Low Accuracy: Indicates issues with the agent's reasoning, knowledge, or prompt design.
High Latency: Suggests inefficient tool usage or complex chains.
High Cost: Might mean too many LLM calls or using expensive models unnecessarily.

Use these insights to iteratively improve your agent.

Check Your Understanding

Based on what we've learned, which of the following are important considerations when evaluating the performance of an AI agent?

Recap: Evaluating Agent Performance

In this lesson, you learned about the importance of evaluating your AI agents and key metrics to consider.

We covered accuracy, latency, cost, and robustness as vital KPIs.
You understood the need for an evaluation set (ground truth).
We explored both manual and automated evaluation approaches.

Regular evaluation is key to building reliable and effective AI agents. Keep refining your agents based on the insights you gain!

よくある質問

「エージェントのパフォーマンス評価」レッスンは無料ですか？

はい。「エージェントのパフォーマンス評価」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Agents with LangChain & Autonomous Workflowsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

「エージェントのパフォーマンス評価」で何を学びますか？

AIエージェントの有効性と信頼性を定量的に評価する手法や指標を学びます。ブラウザで直接実行するハンズオンコードでAI Agents with LangChain & Autonomous Workflowsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

AI Agents with LangChain & Autonomous Workflowsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのAI Agents with LangChain & Autonomous Workflowsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。これはレッスン3/4です。

「エージェントのパフォーマンス評価」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このAI Agents with LangChain & Autonomous Workflowsレッスンでコードを書いて実行できますか？

はい。すべてのAI Agents with LangChain & Autonomous Workflowsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

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