Autonomous AI Fixes Quickstart

Set up your autonomous engineer in under 10 minutes. Once configured, it will automatically detect quality issues in your AI, generate fixes, test them, and create pull requests with proven improvements.

Setting Up Your Autonomous Engineer

Getting your autonomous engineer up and running is straightforward. The key is connecting it to your GitHub repository so it can create pull requests with fixes when issues are detected.

Connect to GitHub

Run the GitHub integration command in your project directory:

handit-cli github

The CLI will walk you through installing the Handit GitHub app on your repository and configuring the necessary permissions. This allows your autonomous engineer to create pull requests when it finds and validates fixes for quality issues.

Once the setup completes, your autonomous engineer is active and monitoring your AI around the clock.

How Your Autonomous Engineer Operates

After setup, your autonomous engineer works continuously in the background without any intervention from you. Here’s what happens:

Continuous Quality Monitoring: Your autonomous engineer monitors evaluation scores across all your AI interactions. When it notices patterns like declining empathy scores, accuracy drops, or response quality issues, it immediately begins investigating the root cause.

Intelligent Fix Generation: Rather than just alerting you to problems, your autonomous engineer analyzes what’s different between your successful and failed interactions. It then generates targeted improvements to your system prompts that address the specific issues it identified.

Thorough Testing: Before creating any pull requests, your autonomous engineer tests its fixes against real production data. It runs hundreds of actual user interactions through both your current prompt and the improved version, only proceeding when it has statistical confidence that the fix genuinely improves performance.

Professional Pull Requests: When a fix is validated, your autonomous engineer creates a detailed pull request that explains what was wrong, how the fix addresses it, and provides concrete evidence of improvement with before/after metrics.

A Day in the Life of Your Autonomous Engineer

Here’s what a typical issue detection and resolution cycle looks like:

Monday Morning: Your autonomous engineer notices that customer service empathy scores have dropped from 4.2 to 3.7 over the weekend. It begins analyzing the failed interactions to understand why.

Monday Afternoon: After analyzing patterns, it determines that the system prompt lacks emotional context for handling frustrated customers. It generates an improved prompt with better empathy guidelines.

Tuesday Morning: The fix has been tested against 500 real customer interactions overnight. Results show a 24% improvement in empathy scores with 95% statistical confidence. Your autonomous engineer creates a pull request.

Tuesday: You receive the PR notification, review the changes and metrics, and merge the improvement. Customer satisfaction scores begin improving immediately.

The Result: What used to be a multi-day debugging process that required your direct involvement is now handled automatically while you focus on building new features.

Monitoring Your Autonomous Engineer

You can track your autonomous engineer’s work through the Handit dashboard:

Agent Performance: Shows overall trends in your AI’s quality metrics and highlights when issues are detected and resolved. You’ll see clear before/after comparisons showing the impact of each fix.

Release Hub: Provides detailed analysis of each improvement, including the A/B test results that validated the fix before the pull request was created. This gives you full transparency into how your autonomous engineer makes decisions.

GitHub Integration: All activity appears in your normal GitHub workflow. Pull requests from your autonomous engineer look and function exactly like those from human teammates, maintaining your existing code review and deployment processes.

What to Expect

Your autonomous engineer typically creates 2-4 pull requests per month for most AI applications, depending on how actively your AI is being used and how much room there is for improvement. Each PR represents a meaningful quality improvement that’s been thoroughly tested.

The improvements become more targeted over time as your autonomous engineer learns your AI’s specific patterns and challenges. Early fixes might address broad issues like tone or completeness, while later improvements often tackle nuanced problems specific to your use case.

Most teams find that after a few weeks, their AI quality stabilizes at a higher level with fewer issues requiring attention. Your autonomous engineer continues monitoring but focuses on maintaining quality and making incremental improvements rather than fixing major problems.

Next Steps

Your autonomous engineer is now working 24/7 to keep your AI performing at its best. Here’s what you can explore:

Monitor Progress: Check your Agent Performance dashboard  to see your autonomous engineer’s impact on your AI quality over time.

Advanced Configuration: Learn more about customizing your autonomous engineer’s behavior in our detailed GitHub integration guide.

Get Help: If you have questions or need assistance, visit our Support page or join our community.

Troubleshooting

GitHub Setup Issues: If you encounter problems during setup, ensure you have admin access to the repository and try running handit-cli github again. The CLI will guide you through any missing steps.

No Pull Requests: If you don’t see pull requests after a few days, it likely means your AI is performing well and no significant issues have been detected. You can check the Agent Performance dashboard to confirm your quality metrics are stable.

Permission Problems: Make sure the Handit GitHub app has the necessary permissions in your repository settings. Re-running handit-cli github will help identify and fix any permission issues.

For additional help, our detailed troubleshooting guide covers common scenarios and solutions.