Louis Sanna

Posted on Dec 12

Self-Correcting AI Agents: How to Build AI That Learns From Its Mistakes

#langchain #fastapi #llm #python

Introduction

What if your AI agent could recognize its own mistakes, learn from them, and try again — without human intervention? Welcome to the world of self-correcting AI agents.

Most AI models generate outputs in a single attempt. But self-correcting agents go further. They can identify when an error occurs, analyze the cause, and apply a fix — all in real time. Think of it as an AI with a built-in "trial and error" mindset.

In this blog, you’ll learn:

What self-correction means for AI agents.
How to build an agent that adapts to mistakes.
How to apply the reflection pattern in agent design.

By the end, you’ll know how to design AI agents that not only fail gracefully but also improve on every attempt.

1️⃣ What is a Self-Correcting Agent?

A self-correcting agent is an AI system that can recognize its own failures and attempt a new strategy. If the initial approach doesn't work, the agent re-evaluates and tries an alternative path.

Analogy:

Imagine asking a chef to bake a cake, but they use too much sugar the first time. A standard AI would keep making the same mistake. But a self-correcting AI would notice the error, reduce the sugar next time, and adjust until the cake tastes perfect.

Why Do Self-Correcting Agents Matter?

Most AI tools (like ChatGPT) can only give you a single response. If it's wrong, you have to manually ask it to "try again." But a self-correcting agent can autonomously retry.

🛠️ Example Use Case:

An AI is asked to write a Python function that calculates Fibonacci numbers.

Attempt 1: The AI writes a slow recursive function.

Self-Correction: It notices that recursion is too slow.

Attempt 2: The AI rewrites the function using dynamic programming, making it faster.

2️⃣ Key Techniques for Self-Correction

How do we make an agent self-aware enough to recognize its mistakes? Here are three main techniques:

1. Error Detection

Identify if the result is "wrong" (like an API call failure, incorrect output, or bad performance).
Use error codes, exceptions, or test cases to detect failures.

2. Reflection

Agents reflect on their decisions, ask, "What went wrong?", and plan their next step.
Reflection can be achieved by logging errors, tracking unsuccessful API calls, or re-evaluating response quality.

3. Retry Logic

After reflection, agents retry with an improved strategy.
This might mean switching API providers, using more efficient logic, or applying a backup approach.

💡 Pro Tip:

Error logs can be fed back into the AI model to improve future performance.

3️⃣ Self-Correction in Practice

Let’s build a self-correcting AI agent using Python and FastAPI.

🧑‍💻 Step 1: The Problem

We want an AI agent that can generate a Python function. If the function fails to run or produces the wrong output, the agent will automatically correct itself.

Problem: Write a Fibonacci function that calculates the 10th Fibonacci number.

Challenge: If the agent generates a recursive version (which is slow), it should recognize this and rewrite it using dynamic programming.

🧑‍💻 Step 2: Set Up the Agent

Install the necessary dependencies:

pip install openai fastapi uvicorn

🧑‍💻 Step 3: Write the Agent

Here’s how the agent works:

It generates a Python function using OpenAI’s API.
It runs the function to check if it works.
If the function fails (slow, wrong, or error), it reflects and corrects the approach.

Code Implementation

import openai
import time
import asyncio

# 🔐 Replace with your OpenAI API key
openai.api_key = "your_openai_api_key_here"

# 🎉 Step 1: Ask the AI to generate a Fibonacci function
async def generate_fibonacci_function():
    prompt = "Write a Python function to calculate the 10th Fibonacci number."
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    function_code = response['choices'][0]['message']['content']
    return function_code

# 🧪 Step 2: Test the function to see if it works
def test_fibonacci_function(function_code):
    try:
        exec(function_code)  # Run the function in a safe execution environment
        result = eval("fibonacci(10)")  # Call the function with n=10
        if result == 55:  # Correct Fibonacci value for n=10
            return "success", result
        else:
            return "wrong_output", result
    except Exception as e:
        return "error", str(e)

# 🌀 Step 3: Self-Correct by asking for a new version of the function
async def self_correct_function():
    max_attempts = 3
    for attempt in range(max_attempts):
        print(f"🟢 Attempt {attempt + 1}")

        # Generate a new Fibonacci function
        function_code = await generate_fibonacci_function()
        print(f"Generated function:\n{function_code}\n")

        # Test the function to see if it works
        status, result = test_fibonacci_function(function_code)
        if status == "success":
            print(f"✅ Success! Fibonacci(10) = {result}")
            return
        elif status == "wrong_output":
            print(f"❌ Incorrect result: {result}. Asking AI to try a better method.")
        else:
            print(f"💥 Error: {result}. Asking AI to try again.")

    print("❌ Max attempts reached. Could not generate a correct function.")

# 🔥 Run the correction process
asyncio.run(self_correct_function())

4️⃣ How It Works

Generate Function: The AI writes a Python function for Fibonacci.
Run the Function: The agent executes the function and checks the result.
Self-Correct: If the result is wrong, it prompts OpenAI to try again with a smarter approach.

Output Example

🟢 Attempt 1
❌ Incorrect result: 42. Asking AI to try a better method.

🟢 Attempt 2
💥 Error: NameError: name 'fibonacci' is not defined. Asking AI to try again.

🟢 Attempt 3
✅ Success! Fibonacci(10) = 55

5️⃣ Key Patterns in Self-Correcting Agents

Error Detection: Look for incorrect output, slow performance, or exceptions.
Reflection: Log the problem. Why did it fail?
Retry Logic: Call a new version of the function, but smarter this time.

💡 Pro Tip:

Use a feedback loop to let the agent learn from mistakes. Feed logs back into the agent to help it recognize common issues.

6️⃣ When Should You Use Self-Correcting Agents?

Self-correcting agents are useful in cases where failure is frequent, and manual intervention is costly.

API Calls: Retry if an API fails.
Code Generation: Re-generate code if it throws errors.
Data Analysis: Fix incorrect predictions in ML models.

7️⃣ Benefits of Self-Correcting Agents

Problem	Solution
Agent gets it wrong	Retry with a better approach
API request fails	Retry with exponential backoff
Code generation error	Use a smarter prompt

8️⃣ Take It to the Next Level

Use a Cache: Store successful outputs so the agent doesn’t start from scratch.
Add Feedback Loops: If a function fails often, feed logs into a training process.
Track Agent Confidence: If the agent is unsure, have it run test cases.

9️⃣ Wrapping Up

You now have the blueprint for a self-correcting agent that can write, test, and fix Python functions. Here’s what we covered:

The 3 pillars of self-correction: Error Detection, Reflection, Retry Logic.
How to build an agent that generates and tests Python functions.
Best practices for building smarter, more reliable agents.

💪 Challenge:

Build a self-correcting agent that not only generates code but evaluates runtime performance. If the function is too slow, have it re-write the function for optimization.

Want to learn more about building Responsive LLMs? Check out my course on newline: Responsive LLM Applications with Server-Sent Events

I cover :

How to design systems for AI applications
How to stream the answer of a Large Language Model
Differences between Server-Sent Events and WebSockets
Importance of real-time for GenAI UI
How asynchronous programming in Python works
How to integrate LangChain with FastAPI
What problems Retrieval Augmented Generation can solve
How to create an AI agent ... and much more.

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