Testing the Agent with Examples › Analyzing Test Results
Michael BrenndoerferAugust 2, 20252 min read
AI Agent HandbookAll chapters Part 11 of 15Evaluation - Measuring Your Agent's Performance
Testing the Agent with Examples › Analyzing Test ResultsOne section · Sections in this chapter4 / 11 ↓
Sections in this chapter11
Once you've run your tests, you need to understand what they're telling you. Let's create a simple summary function:
In[7]:
Codedef summarize_results(results):
"""
Generate a summary of test results.
"""
total = len(results)
passed = sum(1 for r in results if r["passed"])
failed = total - passed
print(f"\n{'=' * 50}")
print("Test Summary")
print(f"{'=' * 50}")
print(f"Total tests: {total}")
print(f"Passed: {passed} ({passed / total * 100:.1f}%)")
print(f"Failed: {failed} ({failed / total * 100:.1f}%)")
# Break down by category
categories = {}
for result in results:
# Find the test case to get its category
test_case = next(
(tc for tc in test_suite if tc["id"] == result["test_id"]), None
)
if test_case:
cat = test_case["category"]
if cat not in categories:
categories[cat] = {"total": 0, "passed": 0}
categories[cat]["total"] += 1
if result["passed"]:
categories[cat]["passed"] += 1
print("\nBy Category:")
for cat, stats in categories.items():
success_rate = stats["passed"] / stats["total"] * 100
print(
f" {cat}: {stats['passed']}/{stats['total']} ({success_rate:.1f}%)"
)
# List failed tests
if failed > 0:
print("\nFailed Tests:")
for result in results:
if not result["passed"]:
print(f" {result['test_id']}: {result['input'][:50]}...")This summary shows the overall pass rate and breaks down performance by category. You might discover that your agent is excellent at tool use but struggles with planning tasks. That tells you exactly where to focus your improvement efforts.
When you run this summary on our example results, you'll see:
==================================================
Test Summary
==================================================
Total tests: 5
Passed: 4 (80.0%)
Failed: 1 (20.0%)
By Category:
tool_use: 1/1 (100.0%)
memory: 2/2 (100.0%)
reasoning: 1/1 (100.0%)
planning: 0/1 (0.0%)
Failed Tests:
planning_001: Find today's weather and recommend what I should we...This tells us that our agent handles basic capabilities well but needs work on planning tasks. That's actionable information.