Autonomous AI Fixes

Your autonomous engineer that works 24/7. Handit automatically detects quality issues, generates better prompts, tests them, and creates pull requests with proven improvements—like having an on-call engineer dedicated to your AI.

Stop Being Your AI’s On-Call Engineer

Picture this: It’s 2 AM and your phone buzzes. Your customer service AI is giving unhelpful responses, and angry customers are flooding your support channels. You stumble out of bed, spend hours debugging prompts, make a guess at what might fix it, and deploy changes hoping they work. Sound familiar?

This is the reality for most AI teams. You’re not building features—you’re constantly firefighting AI quality issues. Every time you fix one problem, three more seem to pop up. You’re tweaking prompts based on intuition rather than data, with no way to safely test if changes actually improve things before they go live.

What if instead of waking up to fix problems, you woke up to pull requests with proven solutions already tested and ready to merge?

How Your Autonomous Engineer Works

Think of it as having a dedicated engineer on your team whose only job is to monitor and improve your AI. Here’s how it operates:

Continuous Monitoring: Your autonomous engineer never sleeps. It constantly watches your AI’s performance through evaluation scores, tracking quality trends and immediately spotting when something goes wrong. Whether it’s a drop in empathy scores, accuracy issues, or response quality problems, it catches them before they impact your users significantly.

Intelligent Problem Solving: When an issue is detected, your autonomous engineer doesn’t just alert you—it investigates. It analyzes what’s different between your successful and failed interactions, identifies the root cause (maybe your system prompt is missing context, or the tone is off), and generates a targeted fix that addresses the specific problem.

Safe Testing: Before bothering you with a fix, your autonomous engineer tests it thoroughly. It runs the improved prompt against real production data in the background, comparing results with your current version. Only when it’s confident the fix actually works (with statistical significance) does it create a pull request.

Professional Pull Requests: When you receive a PR from your autonomous engineer, it’s just like working with a human colleague. The pull request includes a clear explanation of what was wrong, what the fix does, and concrete evidence that it works—complete with before/after metrics and examples.

What Your Pull Requests Look Like

When your autonomous engineer detects an issue and creates a fix, you’ll receive a pull request that looks like this:

Example: Customer Service Empathy Fix

## 🤖 Autonomous AI Fix: Improve Customer Service Empathy
 
### Issue Detected
Empathy scores dropped from 4.2/5.0 to 3.7/5.0 over the past week, causing a 15% 
decrease in customer satisfaction. Root cause analysis shows the system prompt lacks 
emotional context and empathy instructions.
 
### Fix Applied
Updated `src/agents/customer_service/system_prompt.py` lines 23-31 to include 
empathy guidelines and emotional context awareness.
 
### Results Validated
- Empathy Score: 3.7/5.0 → 4.6/5.0 (+24% improvement)
- Overall Quality: 4.1/5.0 → 4.4/5.0 (+7% improvement)
- Statistical Confidence: 95% (tested on 500 real interactions)
 
Ready to merge when you approve!

Seamless Integration with Your Workflow

Your autonomous engineer doesn’t disrupt your development process—it enhances it. You continue writing code, deploying through your normal CI/CD pipeline, and reviewing pull requests as usual. The only difference is that some of those pull requests come from your autonomous engineer with proven AI improvements.

No New Tools to Learn: Everything happens through GitHub pull requests. Your team already knows how to review and merge PRs, so there’s no learning curve.

Your Standards Apply: Just like with human teammates, you review the changes, ask questions if needed, and merge when you’re satisfied. You maintain complete control over what goes into production.

Continuous Improvement: Unlike human engineers who need sleep, your autonomous engineer works around the clock. Your AI gets better continuously without you having to think about it, freeing you to focus on building new features rather than maintaining existing ones.

Real Impact on Your Development

Teams using autonomous AI fixes report a fundamental shift in how they work with AI. Instead of spending 30-40% of their time on AI maintenance and prompt engineering, they focus on building features. Quality issues get caught and fixed automatically, often before they significantly impact users.

The autonomous engineer learns your AI’s patterns and becomes more effective over time. It understands what types of fixes work for your specific use cases and generates increasingly targeted solutions.

Most importantly, you sleep better. No more 2 AM alerts about AI quality issues. No more weekend debugging sessions. Your autonomous engineer handles the monitoring and fixing while you focus on what matters most—growing your product.

Get Started

Ready to stop being your AI’s on-call engineer?

Set up your autonomous engineer in under 10 minutes and start receiving pull requests with proven AI improvements.