AI Agent Evaluation: How Do You Know an AI Agent Actually Works?
AI agents can generate convincing answers. But how can we verify that they are correct, reliable, and using tools appropriately?
Discover how AI Agent Evaluation helps us systematically test agent behavior.
Understand → Apply → Build → Test
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🎬 Watch the Short Lesson
Discover how to evaluate whether an AI agent provides accurate answers, uses the correct tools, and handles missing information.
1. What Is AI Agent Evaluation?
AI Agent Evaluation systematically checks whether an AI agent behaves as intended on representative tasks.
It examines not only the final response, but also:
- Answer accuracy and correctness
- Appropriate tool usage
- Successful task completion
- Safety and permission compliance
- Handling of errors and missing information
Simple explanation: Instead of asking, "Does this answer sound good?", we ask:
Did the agent do the right thing, using the right information and appropriate tools?
2. How Does AI Agent Evaluation Work?
🎯 Define Expected Behavior
↓
🧪 Create Test Cases
↓
🤖 Run the AI Agent
↓
📊 Compare Results with Expectations
↓
🔍 Analyze Failures
↓
🔄 Improve and Retest
Evaluation should cover normal cases, unexpected inputs, ambiguous questions, missing data, and potentially unsafe requests.
3. Practical Applications
- Customer Support: Verify accurate responses grounded in customer records.
- Research Agents: Evaluate source credibility, completeness, and factual correctness.
- Workflow Automation: Check whether agents use permitted tools and complete the intended tasks.
- Software Engineering: Evaluate generated code, test outcomes, and change safety.
- Enterprise AI: Monitor reliability, operational risk, latency, and costs.
4. Industry Example: Retail Order Support
Illustrative scenario: An e-commerce company uses an AI customer-support assistant to check order status.
The agent should retrieve verified order information rather than invent an answer.
Evaluation Test Cases
📦 Order 1042
Expected: Shipped
📦 Order 1043
Expected: Processing
❓ Order 9999
Expected: Order not found
An evaluator checks whether the agent provides the correct order status and calls the order lookup tool with the appropriate order ID.
This is an educational example using fictional orders, not a verified deployment by a specific retailer.
5. Build an AI Agent Evaluation Project
Let's create an AI agent in Python and evaluate its responses against predefined test cases.
Prerequisites
- Python 3.10 or later
- OpenAI API access
- OpenAI API key configured securely
- OpenAI Agents SDK
Step 1: Install the SDK
pip install openai-agents
Step 2: Create the Evaluation Script
Save the following code as evaluate_order_agent.py.
import asyncio
from agents import Agent, Runner, function_tool
# Fictional order records
ORDERS = {
"1042": "Shipped",
"1043": "Processing"
}
# Record tool calls for evaluation
TOOL_CALLS = []
@function_tool
def get_order_status(order_id: str) -> str:
"""Look up a fictional order by its ID."""
TOOL_CALLS.append(order_id)
return ORDERS.get(
order_id,
"Order not found"
)
# Define the AI Agent
agent = Agent(
name="Demo Order Agent",
instructions=(
"Always call get_order_status "
"for order queries. "
"Use the returned status; "
"never guess."
),
tools=[get_order_status],
)
# Define test cases
TESTS = [
("1042", "Shipped"),
("1043", "Processing"),
("9999", "Order not found"),
]
async def main():
passed = 0
for order_id, expected in TESTS:
TOOL_CALLS.clear()
result = await Runner.run(
agent,
f"What is the status of order {order_id}?"
)
response = str(
result.final_output or ""
)
# Check expected answer
answer_ok = (
expected.casefold()
in response.casefold()
)
# Check correct tool usage
tool_ok = order_id in TOOL_CALLS
# Both checks must pass
ok = answer_ok and tool_ok
passed += int(ok)
print(
order_id,
"PASS" if ok else "FAIL"
)
print("Answer:", response)
print("Tool used:", tool_ok)
print(
f"Passed {passed}/{len(TESTS)} tests"
)
asyncio.run(main())
Step 3: Run the Evaluation
Set your
OPENAI_API_KEY
environment variable securely.
Run:
python evaluate_order_agent.py
Step 4: Interpret the Results
For every test, the evaluator checks:
- Did the agent provide the expected answer?
- Did the agent use the required lookup tool?
- Did the tool receive the expected order ID?
A test passes only when the evaluated conditions are satisfied.
This is a simplified teaching evaluator. Substring matching can produce false positives. Production evaluation requires stronger checks, broader datasets, trace inspection, and appropriate human review. Live API behavior has not been verified here.
6. Test and Evaluate Your Agent
- Accuracy: Does the agent return the expected order status?
- Tool Usage: Did it call the order lookup function with the correct order ID?
- Missing Information: Does it avoid inventing the status of an unknown order?
- Repeatability: Does the agent pass the same tests across repeated runs?
- Security: In a real system, is the customer authenticated and authorized to access the requested order?
Evaluation Metric: Pass Rate
Pass Rate = Passed Test Cases ÷ Total Test Cases × 100
For example, if an agent passes all three evaluation cases:
3 ÷ 3 × 100 = 100%
However, a 100% pass rate on three educational examples does not demonstrate production-level reliability.
Real agents require more comprehensive testing across diverse and challenging cases.
7. Review Questions
- Why is evaluating an AI agent different from checking only its final answer?
- How would you evaluate an agent when its tool returns an error?
- What additional tests would a real customer-support agent need?
8. Interactive Quiz
Test your understanding of AI Agent Evaluation with five questions.
9. Official References and Further Learning
What's Next?
AI Agent Guardrails
How can an AI agent stay within its permitted boundaries?
Continue learning to design, build, test, and evaluate reliable AI agents.
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