AI Agents · Lesson

Integration Tests for Agent Pipelines

End-to-end tests against real services in isolated test environments.

What Are Integration Tests for Agents?

Unit tests check individual components in isolation. Integration tests check that multiple components work together correctly in a real or near-real environment.

For agents, this means running the full pipeline — LLM calls, tool execution, data storage — against real or sandboxed services.

End-to-End Test Structure

An end-to-end agent test sends a real query through the entire pipeline and validates the final result. Run these tests in a sandboxed environment — never against your production database or live user data.

import pytest

# Mark as integration test — skipped in fast unit test runs
@pytest.mark.integration
def test_research_agent_full_pipeline():
    from myagent import ResearchAgent

    agent = ResearchAgent(
        openai_api_key='YOUR_TEST_KEY',
        search_api_key='YOUR_TEST_KEY'
    )

    result = agent.run('What is the population of Tokyo?')

    # Structural assertions — not exact string matching
    assert isinstance(result, dict)
    assert result['status'] == 'completed'
    assert 'tokyo' in result['answer'].lower() or 'japan' in result['answer'].lower()
    assert len(result['sources']) >= 1

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