Autonomous AI Fixes

Your AI learns from every mistake and gets better automatically. Handit’s autonomous fixing system analyzes quality issues, generates targeted improvements, and validates fixes before deploying them—transforming every problem into an opportunity for enhancement.

Instead of manually debugging AI quality issues, your autonomous engineer handles the entire improvement process while you focus on building features.

Autonomous AI fixes work by analyzing evaluation data to identify patterns in quality issues, then generating and testing targeted improvements that address the root causes of those problems.

How Autonomous Fixing Works

Your autonomous engineer operates like a skilled debugging partner who never sleeps. When evaluation scores indicate quality issues, it immediately begins investigating what’s going wrong and how to fix it.

Pattern Recognition: Your autonomous engineer continuously analyzes evaluation results, looking for patterns in failed interactions. If empathy scores start declining, it examines which types of conversations are struggling and what specific aspects of responses are falling short.

Root Cause Analysis: Rather than applying generic fixes, your autonomous engineer investigates why problems are occurring. It compares successful and failed interactions to understand what’s different—perhaps your system prompt handles simple questions well but struggles with complex, emotionally charged situations.

Targeted Fix Generation: Based on its analysis, your autonomous engineer creates specific improvements that address the identified issues. If the problem is poor empathy handling, it generates prompt modifications that include better emotional awareness and response patterns for difficult conversations.

Iterative Improvement: Your autonomous engineer doesn’t stop at the first fix. It generates multiple potential improvements, tests them against real data, and iteratively refines its approach until it achieves significant quality improvements with high statistical confidence.

Error Analysis & Root Cause Detection

Evaluator Error Processing

The system analyzes evaluator-flagged errors to understand prompt weaknesses:

Example: Evaluator Error Analysis


Customer Support LLM - Error Analysis

Recent Evaluator Failures (50 flagged responses):

❌ Empathy Failures (18 responses)
Evaluator Feedback:
- "Response lacks acknowledgment of customer frustration"
- "No emotional validation provided"
- "Too transactional, not empathetic"

❌ Completeness Failures (15 responses)  
Evaluator Feedback:
- "Missing key information requested"
- "Didn't address all parts of the question"
- "Incomplete troubleshooting steps"

❌ Tone Failures (12 responses)
Evaluator Feedback:
- "Too formal for casual inquiry"
- "Language doesn't match customer's communication style"
- "Overly technical for general audience"

LLM Root Cause Analysis

Current Prompt Analysis:


Current Production Prompt:
"You are a helpful customer service assistant. 
Respond to customer inquiries with accurate information and clear solutions.
Always be professional and provide step-by-step instructions when appropriate."

LLM Root Cause Analysis:
❌ Missing empathy instructions → Causes empathy failures
❌ No completeness requirements → Causes incomplete responses  
❌ Fixed formality level → Causes tone mismatches
❌ No emotional context awareness → Reduces customer satisfaction

Identified Issues:
1. Prompt lacks emotional intelligence guidance
2. No systematic approach to addressing all question parts
3. Rigid communication style doesn't adapt to context
4. Missing customer sentiment consideration

Prompt Generation & Iteration

Parallel Prompt Generation

The system generates multiple improved prompt versions simultaneously:

Generated Prompt Variations:

Version A: Empathy-Focused


You are a caring customer service specialist who genuinely wants to help.

Before providing solutions:
1. Acknowledge the customer's concern with empathy
2. Validate their feelings if they seem frustrated
3. Show understanding of their situation

Then provide clear, helpful solutions with supportive language.

Version B: Completeness-Focused


You are a thorough customer service assistant.

For every customer inquiry:
1. Read the entire question carefully
2. Identify all parts that need addressing
3. Provide complete information for each part
4. Confirm you've addressed everything requested

Always ensure comprehensive responses.

Version C: Adaptive Tone


You are a customer service assistant who adapts to each customer.

Match your communication style to the customer:
- Formal tone for business inquiries
- Friendly tone for casual questions
- Technical detail for expert users
- Simple language for general audiences

Provide helpful, appropriately-toned responses.

Iterative Improvement Process

Iteration Cycle:


Iteration 1: Initial Analysis
- Analyzed 50 evaluator errors
- Generated 3 prompt versions (A, B, C)
- Each targets specific root cause

Iteration 2: Combined Insights  
- Merged successful elements from versions A, B, C
- Generated hybrid prompt addressing multiple issues
- Added refinements based on error patterns

Iteration 3: Advanced Optimization
- Incorporated feedback from background testing
- Fine-tuned language and structure
- Created final optimized version

Final Optimized Prompt:
"You are an empathetic customer service specialist who provides complete, appropriately-toned responses..."
[Combines empathy, completeness, and adaptive tone elements]

Real-World Example

Customer Support Optimization

Step 1: Error Detection


Evaluator Flagged Issues:
- 23 responses marked as "lacking empathy"
- 15 responses marked as "incomplete"
- 8 responses marked as "inappropriate tone"

Common Error Patterns:
- Direct solutions without acknowledgment
- Missing information in complex queries
- Formal language for casual questions

Step 2: Root Cause Analysis


LLM Analysis of Current Prompt:
"The prompt instructs to be 'professional' but doesn't define what that means in different contexts. It lacks guidance for emotional recognition and doesn't require comprehensive question analysis."

Identified Root Causes:
1. No empathy instructions
2. No completeness checklist
3. Rigid tone requirements

Step 3: Parallel Prompt Generation


Generated 4 prompt versions:
- Version 1: Enhanced empathy focus
- Version 2: Systematic completeness approach  
- Version 3: Context-adaptive tone
- Version 4: Hybrid combining all improvements

Step 4: Iterative Refinement


Iteration Results:
- Version 4 performed best in background testing
- Further refined based on specific error patterns
- Final version addresses all identified root causes

Background Testing Integration

Automatic Validation

Each generated prompt version is automatically tested:


Background Testing Results:

Current Production Prompt:
- Empathy Score: 2.8/5.0
- Completeness Score: 3.1/5.0  
- Tone Appropriateness: 3.4/5.0

Generated Version (Iteration 3):
- Empathy Score: 4.2/5.0 (+50% improvement)
- Completeness Score: 4.0/5.0 (+29% improvement)
- Tone Appropriateness: 4.1/5.0 (+21% improvement)

Statistical Confidence: 95%
Recommendation: Ready for deployment

Continuous Learning

Pattern Recognition

The system learns from successful optimizations:


Learning Insights from Previous Optimizations:

Successful Patterns:
✅ Empathy acknowledgment phrases increase scores by 40-60%
✅ Systematic question analysis improves completeness by 25-35%
✅ Context-adaptive language reduces tone issues by 50%

Applied Learning:
- Prioritize empathy instructions in future prompts
- Include systematic approaches for complex queries
- Add context awareness to all prompt generations

Next Steps

Ready to see how your optimized prompts are tested and deployed?

Learn about Background A/B Testing methodology and how optimizations are validated
Explore SDK Integration & Prompt Fetching to deploy optimizations in your applications
Set up optimization tokens to automatically enable self-improving AI

Self-improving AI activates automatically when you configure optimization tokens. The system will start analyzing evaluator errors and generating improved prompts immediately.