How QA Agent Works (Architecture)

Before contributing, understand what runs when you type python cli.py run --suite smoke.

What you'll learn

• The execution pipeline from CLI command to HTML report
• What each module in qa_agent/ does
• How the agent makes pass/fail decisions
• Which integration points are extension-friendly

Execution pipeline

CLI command
    │
    ▼
cli.py (entry point)
    │  parses --suite / --file / --tag flags
    │
    ▼
runner.py (RunnerService)
    │  resolves test files, manages parallelism (asyncio + semaphore)
    │
    ▼
parser.py (parse_test_case)
    │  reads markdown, extracts title, persona, priority, tags, steps, expected
    │
    ▼
agent_factory.py (build_agent)
    │  creates browser-use Agent with the configured LLM
    │  loads persona session cookies if persona != unauthenticated
    │
    ▼
browser-use Agent (executes steps)
    │  each step → LLM call → browser action
    │  screenshots captured at every step
    │
    ▼
hooks.py (on_step_end, on_done)
    │  captures screenshots, GIFs, video via Playwright
    │
    ▼
reporter.py (generate_report)
    │  builds HTML report from run results
    │
    ▼
linear_reporter.py / supabase_uploader.py (optional)
    │  creates Linear issues for failures, uploads report
    │
    ▼
HTML report on disk

Module responsibilities

How pass/fail works

QA Agent does not use assertions in the traditional sense. Instead:

1. The agent executes every step in the ## Steps section
2. At the end of the run, the agent evaluates the ## Expected section against what it observed
3. If all expected outcomes are satisfied, the test passes. If any are not, the test fails with a structured failure note explaining which expectation was not met and why

This means the AI model is both the executor and the judge. For most UI flows this works well. For precise numeric or time-based assertions ("price must be exactly $29.00"), be explicit in the Expected section to reduce ambiguity.

LLM configuration

The model is set in config.yaml:

llm:
  model: google/gemini-3.1-flash-lite-preview
  base_url: https://openrouter.ai/api/v1

agent_factory.py constructs the ChatOpenAI-compatible client from these values. To swap models, change model in config.yaml. Any OpenRouter-compatible model ID works.

Integration points

The cleanest places to extend QA Agent:

• New integrations (Jira, GitHub Issues, Slack). Add a module alongside linear_reporter.py, implement a report(run_result) function, and call it from runner.py after the run completes
• Custom agent tools. Add browser actions to custom_tools.py (e.g., drag-and-drop, file upload helpers)
• Custom report formats. Replace or extend reporter.py for JSON output, CSV summaries, or custom HTML templates

What's next

Cable 7: Contributing to QA Agent →