Autonomous AI Fixes

Your autonomous engineer creates pull requests with fixes automatically. When quality issues are detected, your autonomous engineer generates improvements, tests them, and creates PRs with proven fixes.

Transform every quality issue into an automatic improvement delivered through your normal GitHub workflow.

How autonomous fixing works

Your autonomous engineer operates continuously in the background, monitoring your AI’s quality and creating fixes when needed.

Issue Detection: By analyzing evaluation scores, your autonomous engineer identifies when quality drops, which interactions fail, and what patterns indicate systemic problems.

Fix Generation: Rather than generic improvements, your autonomous engineer creates targeted system prompt fixes that address the specific issues detected in your AI.

Validation: Before creating any pull requests, fixes are tested against real production data to ensure they actually improve performance with statistical confidence.

Pull Request Creation: When a fix is validated, your autonomous engineer creates a detailed PR with the improved system prompt and comprehensive performance metrics.

What your pull requests look like

When your autonomous engineer detects and fixes an issue, you receive a professional pull request:

## 🤖 Autonomous Fix: Customer Service Empathy Issues
 
### 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 scores.
 
### Root Cause Analysis  
The system prompt lacks emotional context for handling frustrated customers. 
Analysis shows the AI provides technically correct but emotionally tone-deaf 
responses to upset users.
 
### Fix Applied
Updated `src/agents/customer_service/system_prompt.py` to include:
- Emotional awareness guidelines for frustrated customers
- Specific language patterns for empathetic responses
- Context preservation for emotional state throughout conversations
 
### Validation Results
- Empathy Score: 3.7/5.0 → 4.6/5.0 (+24% improvement)
- Customer Satisfaction: 78% → 89% (+11% improvement)  
- Statistical Confidence: 95% (tested on 500 real interactions)
 
Ready to merge when you approve!

This level of detail means you can quickly understand the change, verify it makes sense, and merge with confidence.

Real-world example: Customer service enhancement

Week 1: Your customer service AI maintains solid 4.2/5.0 empathy scores with good customer satisfaction.

Week 2: Evaluation scores show empathy dropping to 3.7/5.0. Customer complaints increase, mentioning the AI feels “robotic.”

Autonomous Investigation: Your autonomous engineer analyzes declining interactions and discovers the AI handles simple questions well but struggles when customers express frustration.

Fix Generation: Based on this analysis, your autonomous engineer creates an improved system prompt with emotional awareness guidelines and empathetic response patterns.

Validation: The improved prompt is tested against 500 real customer interactions that previously scored poorly. Results show empathy scores improving to 4.6/5.0.

Deployment: A pull request is created with the validated improvement, complete with detailed metrics and examples.

The learning process

Your autonomous engineer becomes more effective over time:

Domain Expertise: Early fixes address broad issues like tone or completeness. Over time, improvements become increasingly nuanced and specific to your use case.

Pattern Memory: Your autonomous engineer remembers what types of fixes work well and applies those insights to new problems.

Quality Calibration: It learns your quality standards by observing which fixes you merge and which you reject.

Types of improvements

Your autonomous engineer addresses various quality issues automatically:

Communication Enhancement: Improves clarity, tone, and helpfulness when evaluation shows communication problems.

Accuracy Improvements: Fixes factual errors and incomplete responses when evaluation detects accuracy issues.

Consistency Fixes: Establishes clearer guidelines when your AI provides inconsistent responses to similar questions.

Edge Case Handling: Identifies and improves handling of specific scenarios where your AI struggles.

Getting started

Autonomous fixes activate automatically when you connect GitHub integration to Handit:

No Configuration Required: Your autonomous engineer starts working as soon as it has evaluation data to analyze.

Gradual Enhancement: Early improvements often address obvious quality issues. Over time, fixes become more sophisticated.

Measurable Impact: Track the cumulative effect through your Agent Performance dashboard.

Your AI doesn’t have to stay static. With autonomous fixes, every quality issue becomes an opportunity for automatic improvement through your normal GitHub workflow.