AI Agent Workflows & CI/CD Integration

TAFLEX PY's native Model Context Protocol (MCP) server enables not just local debugging, but fully autonomous AI workflows. By providing a standardized interface (Resources and Tools), you can integrate AI agents into your daily development cycle and your CI/CD pipelines.

This guide covers how to set up AI agents locally using various clients (Gemini CLI, Cursor, Claude Code) and how to deploy an autonomous testing agent in GitHub Actions.

1. Local AI Agent Integration

You can connect your local workspace to an AI Assistant, allowing it to read your framework configuration, debug failing tests, and write code.

Connecting to Gemini CLI

Gemini CLI has built-in support for MCP servers. You can add the TAFLEX MCP server dynamically or via a configuration file.

Option A: CLI Setup Run this from your project root to register the server:

gemini mcp add taflex-mcp "taflex-mcp" --trust

Option B: Manual Setup Create a .gemini/settings.json file in your workspace:

{
  "mcpServers": {
    "taflex-mcp": {
      "command": "taflex-mcp",
      "args": [],
      "trust": true
    }
  }
}

Usage in Gemini CLI: - Type /mcp in the interactive prompt to verify the connection and see available tools. - Ask: "Use the taflex-mcp tools to run the web test suite. If any tests fail, analyze the output and fix the locator in the corresponding test file."

Connecting to IDEs (Cursor / Roo Code)

In Cursor or VS Code extensions like Roo Code (Cline): 1. Go to MCP Settings. 2. Add a new command server. 3. Provide the absolute path to the executable: /absolute/path/to/.venv/bin/taflex-mcp

Connecting to Claude Desktop / Claude Code

Edit your claude_desktop_config.json:

{
  "mcpServers": {
    "taflex-mcp": {
      "command": "/absolute/path/to/project/.venv/bin/taflex-mcp",
      "args": []
    }
  }
}

2. Autonomous GitHub Actions Agent

The true power of MCP is realized when deployed in CI/CD. You can create a GitHub Workflow that triggers an AI agent to autonomously investigate and fix failing tests when a PR is opened or a nightly run fails.

Architecture

The Trigger: A GitHub Action is triggered (e.g., via issue_comment when a user types /fix-tests).
The MCP Server: The action installs taflex-py[mcp] and exposes the framework's capabilities.
The Agent: A lightweight Python script (using LangChain or Anthropic SDK) connects to the MCP server.
The Loop: The agent runs Pytest, reads the failing code, rewrites the file, and runs Pytest again.
The Commit: Once the test passes, the action commits the fix back to the branch.

Example: The GitHub Workflow

Create .github/workflows/ai-agent.yml:

name: Autonomous Test Maintainer

on:
  issue_comment:
    types: [created]

jobs:
  fix-tests:
    # Trigger only if someone comments "/fix-tests" on a PR
    if: github.event.issue.pull_request && contains(github.event.comment.body, '/fix-tests')
    runs-on: ubuntu-latest
    permissions:
      contents: write
      pull-requests: write

    steps:
      - name: Checkout Repository
        uses: actions/checkout@v4
        with:
          ref: ${{ github.event.pull_request.head.ref }}

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.10'

      - name: Install Framework and AI Dependencies
        run: |
          pip install -e ".[all]"
          playwright install --with-deps
          # Install your preferred AI SDK here (e.g., anthropic, mcp)
          pip install anthropic mcp

      - name: Run AI Debugging Agent
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
        # Assuming you have a script that initializes an Anthropic client with the MCP server
        run: python scripts/ci_agent.py

      - name: Commit and Push Fixes
        run: |
          git config user.name "github-actions[bot]"
          git config user.email "github-actions[bot]@users.noreply.github.com"
          git add tests/ src/
          git diff --staged --quiet || (git commit -m "test: AI autonomously fixed test failures" && git push)

      - name: React to Comment
        uses: actions/github-script@v7
        with:
          script: |
            github.rest.reactions.createForIssueComment({
              owner: context.repo.owner,
              repo: context.repo.repo,
              comment_id: context.payload.comment.id,
              content: "rocket"
            });

Example: The Agent Script (`scripts/ci_agent.py`)

Note: This is a conceptual representation of how an agent uses the MCP client to loop through tools.

import os
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def main():
    # 1. Connect to the local Taflex MCP Server
    server_params = StdioServerParameters(
        command="taflex-mcp",
        args=[]
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # 2. Instruct the AI (Conceptual API Call to LLM)
            prompt = """
            You are an autonomous test engineer. 
            Use the 'run_pytest' tool. 
            If it fails, use 'read_project_file' to understand the code, 
            'write_test_file' to fix it, and run pytest again.
            Stop when 'run_pytest' returns Exit Code 0.
            """

            # 3. The LLM Client library would loop here, requesting tool calls
            # from the session and returning the results to the LLM.
            print("Agent loop initiated...")

if __name__ == "__main__":
    asyncio.run(main())

3. Practical Use Cases

Here are examples of prompts you can use with your connected AI Agent to maximize productivity:

The "Self-Healing" Run

"Run the entire test suite using the run_pytest tool. For every failure you encounter, read the traceback, inspect the relevant Page Object in the src/ folder, fix the selector, and re-run the test until the suite is green."

The "Context-Aware" Scaffold

"I need to write a BDD test for a new 'Shopping Cart' feature. Please read docs://guides/bdd-testing to understand how TAFLEX handles Gherkin. Then, scaffold the .feature file and the corresponding step definition Python file in the tests/bdd/ directory."

The Code Quality Enforcer

"Find all files in tests/web/ that were modified recently. Use the format_code tool to run Ruff on them and ensure they comply with our strict typing and formatting standards."