Valutare le prestazioni degli agenti
Apprenda metodi e metriche per valutare quantitativamente l'efficacia e l'affidabilità dei propri agenti AI.
Valutare le prestazioni degli agenti è una lezione AI Agents with LangChain & Autonomous Workflows gratuita su CoddyKit. Questa è la lezione 3 di 4. Puoi leggere la lezione completa qui gratuitamente — poi esercitati direttamente nel browser con un editor di codice integrato e un tutor IA disponibile 24/7. Fa parte del percorso di apprendimento AI Agents with LangChain & Autonomous Workflows, e i tuoi progressi si sincronizzano tra il web e l'app CoddyKit. Il corso AI Agents with LangChain & Autonomous Workflows include 4 lezioni in totale.
Parti di questa lezione non sono ancora state tradotte e vengono mostrate in inglese.
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!
Domande Frequenti
La lezione «Valutare le prestazioni degli agenti» è gratuita?
Sì — il testo completo di «Valutare le prestazioni degli agenti» è gratuito qui sul web. Per esercitarvi in modo interattivo (un editor di codice integrato e un tutor IA 24/7) e sbloccare il resto del corso AI Agents with LangChain & Autonomous Workflows, passa a CoddyKit PRO. Il corso AI Agents with LangChain & Autonomous Workflows include 4 lezioni in totale.
Cosa imparerò in «Valutare le prestazioni degli agenti»?
Apprenda metodi e metriche per valutare quantitativamente l'efficacia e l'affidabilità dei propri agenti AI. Eserciti AI Agents with LangChain & Autonomous Workflows con codice pratico che esegui direttamente nel browser, e un tutor IA 24/7 risponde alle tue domande mentre lavori sulla lezione.
Ho bisogno di esperienza per iniziare AI Agents with LangChain & Autonomous Workflows?
Non è richiesta alcuna esperienza precedente. AI Agents with LangChain & Autonomous Workflows su CoddyKit è strutturato per principianti e studenti avanzati, quindi puoi iniziare da qui o dall'inizio e procedere al tuo ritmo. Questa è la lezione 3 di 4.
Quanto tempo richiede la lezione «Valutare le prestazioni degli agenti»?
La maggior parte delle lezioni CoddyKit richiede circa 5–10 minuti. Ogni lezione è breve e interattiva, quindi fai progressi costanti e riprendi esattamente da dove hai lasciato su web e app.
Posso scrivere ed eseguire codice in questa lezione AI Agents with LangChain & Autonomous Workflows?
Sì. Ogni lezione AI Agents with LangChain & Autonomous Workflows include un editor di codice integrato, quindi scrivi ed esegui codice reale direttamente nel tuo browser e ricevi feedback istantaneo dall'IA — nessuna configurazione locale necessaria.
Tutte le lezioni di questo corso
- LangSmith per tracing e monitoraggio
- Debugging dei processi di ragionamento degli agenti
- Valutare le prestazioni degli agenti
- Utilizzo dei token e monitoraggio dei costi