A very simple way to build feedback loops for your agents (Noob Guide)

Everyone talks about feedback loops for AI agents like it's rocket science. MLOps pipelines, A/B testing frameworks, human-in-the-loop annotation systems, vector databases for embeddings... I found a simpler way.

The Problem

My sales agent was misclassifying user messages. Someone says "sorry not 41 it's 32" (changing their shoe size), and the AI thought they were negotiating.

I needed a way to:

• Catch these mistakes
• Use them to improve the AI
• Without building a complex system

The Solution: Dual Confidence Thresholds

Instead of just using the AI's answer, I ask for a confidence score too.

def get_user_intent_llm(message, possible_intents):
    prompt = f"""
    Classify this message: "{message}"
    Valid intents: {possible_intents}
    
    Respond in this format:
    INTENT: <your classification>
    CONFIDENCE: <0.0 to 1.0>
    """
    
    response = llm.invoke(prompt)
    intent, confidence = parse_response(response)
    
    # LOG EVERYTHING
    print(f"INTENT: {intent}, CONFIDENCE: {confidence}, MESSAGE: '{message}'")
    
    return intent, confidence

Then I use two thresholds:

HIGH_THRESHOLD = 0.8
LOW_THRESHOLD = 0.5
if confidence >= HIGH_THRESHOLD:
    # AI is confident, use the intent
    use_intent(intent)
    
elif confidence >= LOW_THRESHOLD:
    # AI is unsure, use intent BUT LOG IT
    log_edge_case(message, intent, confidence)  # <-- This is your feedback loop
    use_intent(intent)
    
else:
    # AI has no idea, ask for clarification
    ask_user_to_clarify()

The Feedback Loop

That log_edge_case() function is your entire feedback loop. It writes to a simple file:

def log_edge_case(message, intent, confidence):
    with open("edge_cases.txt", "a") as f:
        f.write(f"{confidence:.2f} | {intent} | {message}\n")

After a week, your file looks like this:

0.65 | negotiate | sorry not 41 it's 32
0.58 | yes | no problem boss
0.72 | location | I stay for Lekki
0.55 | other | can I exchange if size doesn't fit?

How to Use It

Every few days:

1. Open edge_cases.txt
2. Review the messages and their classifications
3. Ask yourself: "Was the AI right?"
4. Collect the corrected examples for fine-tuning

These logged edge cases become your training data. When you have enough (50-100 examples), you can fine-tune your model to handle these specific patterns better.

Why This Works

• No complex infrastructure - It's just a text file
• You see real user data - Not synthetic test cases
• Medium confidence = edge cases - These are exactly the examples you need to improve
• Gradual improvement - Each week you add a few examples, AI gets smarter

The Full Picture

User Message
     ↓
Intent Classification (with confidence)
     ↓
┌─────────────────────────────────────┐
│ HIGH (>0.8)  → Use directly         │
│ MEDIUM (0.5-0.8) → Use + LOG        │  ← Your feedback loop
│ LOW (<0.5)   → Ask clarification    │
└─────────────────────────────────────┘
     ↓
Review logs weekly
     ↓
Add examples to system prompt
     ↓
Repeat