Unleash Your Inner Architect: Build a Self-Correcting AI Agent for Smarter Feature Scaffolding

In the whirlwind world of software development, we often find ourselves caught between the thrill of building innovative features and the grind of repetitive, boilerplate tasks. Whether it's setting up a new component, spinning up an API endpoint, or configuring testing infrastructure, these necessary evils consume valuable time and mental energy that could be better spent on core logic and problem-solving. But what if we could offload these tasks to an intelligent assistant that not only generates the required code but also learns, adapts, and even *corrects its own mistakes*?

Welcome to the era of the self-correcting AI agent. This isn't just about using ChatGPT to spit out a function; it's about building an autonomous entity that can understand context, interact with your codebase, and iterate on its output until it meets your predefined criteria. Today, we're going to dive deep into how you can construct such an agent to automate one of the most common development headaches: feature scaffolding.

The Perennial Problem: Boilerplate Blues

Every developer knows this scenario:

• You need a new React component. It needs a `.tsx` file, a `.test.tsx` file, and maybe a `.stories.tsx` for Storybook.
• You're adding a new REST API endpoint. It requires a new route definition, a controller function, a service layer method, and a database schema update.
• Starting a new microservice means setting up a project structure, a `Dockerfile`, a CI/CD pipeline configuration, and basic health checks.

While frameworks and CLI tools like Create React App or Next.js's CLI offer some help, they often provide generic templates that still require significant manual tweaking. The process is prone to:

• Inconsistency: Different developers might set up similar features slightly differently, leading to varied code styles and structures.
• Time Sink: Copy-pasting, renaming, and manually wiring up boilerplate is tedious and time-consuming.
• Cognitive Load: Every time you start a new feature, you switch contexts from the *what* to the *how to set up*.
• Error Proneness: Small typos or forgotten imports can lead to frustrating debugging sessions later.

These issues don't just slow us down; they detract from the joy of building. Our goal is to shift from being human scaffolding machines to being architects, guiding intelligent systems to do the heavy lifting.

The Intelligent Solution: Self-Correcting LLM Agents

The core idea behind a self-correcting LLM agent for development is simple yet powerful: instead of a single prompt-response interaction, we establish an iterative loop. The agent plans, executes, receives feedback, and then refines its plan or execution based on that feedback. This mimics how a human developer would approach a task.

Here's a breakdown of the key components such an agent relies on:

1. The Orchestrator: This is the brain of your agent. It defines the workflow, manages the execution of tasks, and facilitates communication between different components. It's essentially your custom "agent loop."
2. The Large Language Model (LLM): The intelligence. This could be OpenAI's GPT models, Anthropic's Claude, or open-source alternatives like Llama 3. The LLM's role is to generate code, interpret feedback, and formulate plans or corrections.
3. Tools: These are the agent's "hands." They allow the LLM to interact with the real world. For feature scaffolding, tools might include:
   • File System Access: To read existing files, write new ones, and create directories.
   • Linter/Formatter: Tools like ESLint, Prettier, or Black to check for code style and potential errors.
   • Test Runner: Jest, Pytest, Mocha, etc., to execute generated tests and report failures.
   • Code Interpreter: To execute small snippets or validate syntax.
   • Git: To check changes or revert if necessary.
4. Memory/Context: The agent needs to remember previous interactions, the task's history, and the state of the codebase. This allows it to maintain coherence and learn over time.
5. Feedback Loop: This is the *self-correction* mechanism. It's how the agent knows if its output is successful or needs improvement. Feedback can come from executed tests, linter reports, compiler errors, or even human input.

By combining these elements, we can build an agent that doesn't just guess; it *learns* and *validates* its own work.

Step-by-Step Guide: Building a React Feature Scaffolding Agent

Let's walk through a practical example: building an agent that can scaffold a new React component, complete with a test file and a Storybook story, based on a natural language prompt. We'll focus on the conceptual flow and a simplified code structure.

1. Define the Goal and Success Criteria

Our agent should take a prompt like: "Create a new React component named `UserCard` that displays `name` and `email` props. Include a Jest test file and a Storybook story. Place it in `src/components/UserCard`."

Success criteria:

• `src/components/UserCard/UserCard.tsx` exists and renders `name` and `email`.
• `src/components/UserCard/UserCard.test.tsx` exists, passes, and includes basic prop testing.
• `src/components/UserCard/UserCard.stories.tsx` exists and displays the component.
• All generated code adheres to project ESLint rules and passes Jest tests.

2. Choose Your Stack

For this example, we'll use:

• Agent Framework: LangChain (Python or JS, highly recommended for its tooling around agents, LLMs, and tools) or build a custom orchestrator with simple API calls.
• LLM: OpenAI's GPT-4 (for its superior code generation and instruction following).
• Tools: Node.js `fs` module, `child_process.exec` (to run `eslint` and `jest` commands).
• Feedback Mechanism: Parsing `eslint` and `jest` command outputs.

3. Design the Agent's Workflow (The Orchestrator Logic)

This is where the "self-correcting" magic happens. Our agent will follow a `Plan -> Execute -> Reflect -> Correct` loop:

1. Initialize: Receive the user prompt.
2. Plan: Ask the LLM to break down the prompt into actionable steps (e.g., "create component file", "create test file", "create story file", "write component code", "write test code", "write story code", "run lint", "run tests").
3. Execute: For each step in the plan:
   • Use the LLM to generate the specific code or file content.
   • Use file system tools to create/write files.
4. Reflect & Validate:
   • Run `eslint` on the newly created files.
   • Run `jest` tests on the generated test files.
5. Correct (if needed):
   • If `eslint` reports errors or `jest` tests fail, feed the *error messages* and the *current code* back to the LLM.
   • Instruct the LLM to identify the issues and generate *corrected* code.
   • Go back to the "Execute" step for the corrected files.
   • Keep track of retry attempts to prevent infinite loops.
6. Finalize: Once all checks pass, report success and the generated file paths.

4. Implementing the Core Orchestrator (Simplified Example)

Here's a conceptual Python-like snippet illustrating the agent loop. In a real application, you'd use a library like LangChain for robust tool integration and prompt management.

import os
import subprocess
from openai import OpenAI # Assuming you're using OpenAI's API

class FeatureScaffoldingAgent:
    def __init__(self, llm_client, workspace_path="./src/components"):
        self.llm = llm_client
        self.workspace_path = workspace_path
        self.max_retries = 3

    def _call_llm(self, prompt, system_message=None):
        messages = []
        if system_message:
            messages.append({"role": "system", "content": system_message})
        messages.append({"role": "user", "content": prompt})
        
        response = self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=messages,
            temperature=0.7
        )
        return response.choices[0].message.content

    def _run_command(self, command, cwd=None):
        try:
            result = subprocess.run(
                command,
                cwd=cwd,
                shell=True,
                capture_output=True,
                text=True,
                check=True # Raise an exception for non-zero exit codes
            )
            return True, result.stdout
        except subprocess.CalledProcessError as e:
            return False, e.stderr # Return error message

    def scaffold_feature(self, user_prompt):
        print(f"[AGENT] Starting scaffolding for: {user_prompt}")
        
        # Initial planning phase
        plan_prompt = (
            f"Based on the user request '{user_prompt}', outline a detailed plan to create the necessary files "
            f"for a React component, its Jest test, and a Storybook story. "
            f"Specify file paths and the content to generate for each. "
            f"List steps clearly."
        )
        initial_plan = self._call_llm(plan_prompt, "You are an expert React developer planning a feature.")
        print(f"[AGENT] Initial Plan:\n{initial_plan}\n")

        # In a real scenario, you'd parse this plan into executable steps.
        # For simplicity, let's assume the LLM generates all files and then we validate.
        
        component_name = self._extract_component_name(user_prompt) # Helper to get 'UserCard'
        component_dir = os.path.join(self.workspace_path, component_name)
        os.makedirs(component_dir, exist_ok=True)
        
        file_paths = {
            "component": os.path.join(component_dir, f"{component_name}.tsx"),
            "test": os.path.join(component_dir, f"{component_name}.test.tsx"),
            "story": os.path.join(component_dir, f"{component_name}.stories.tsx")
        }
        
        for attempt in range(self.max_retries):
            print(f"[AGENT] Attempt {attempt + 1} to generate code.")
            
            generation_prompt = (
                f"Based on the request: '{user_prompt}', generate the full content for "
                f"'{component_name}.tsx', '{component_name}.test.tsx', and '{component_name}.stories.tsx'. "
                f"Ensure proper React and TypeScript syntax. "
                f"Provide each file's content clearly separated by markers like <FILE_UserCard.tsx>...</FILE_UserCard.tsx>."
            )
            generated_content = self._call_llm(generation_prompt, "You are an expert React/TypeScript developer.")
            
            # Parse generated_content and write to files
            # (Simplified: In real life, use regex/parsing to extract content for each file)
            # For this example, let's assume it generated all 3 files correctly.
            
            # Dummy file creation for demonstration
            self._write_dummy_files(file_paths, generated_content) 

            # --- Validation Loop ---
            lint_success, lint_output = self._run_command(f"eslint {component_dir} --fix")
            if not lint_success:
                print(f"[AGENT] Linter failed:\n{lint_output}\n")
                correction_prompt = (
                    f"The following ESLint errors were found:\n{lint_output}\n"
                    f"Here's the current code: [Provide all generated code here]\n"
                    f"Please correct the code for '{component_name}.tsx', '{component_name}.test.tsx', "
                    f"and '{component_name}.stories.tsx' to fix these issues. "
                    f"Provide the full corrected content for each file."
                )
                generated_content = self._call_llm(correction_prompt, "You are an expert at fixing React/TypeScript linting errors.")
                self._write_dummy_files(file_paths, generated_content) # Overwrite with corrected code
                continue # Retry validation

            test_success, test_output = self._run_command(f"jest {component_dir}")
            if not test_success:
                print(f"[AGENT] Tests failed:\n{test_output}\n")
                correction_prompt = (
                    f"The following Jest tests failed:\n{test_output}\n"
                    f"Here's the current code: [Provide all generated code here]\n"
                    f"Please correct the code for '{component_name}.tsx', '{component_name}.test.tsx', "
                    f"and '{component_name}.stories.tsx' to make the tests pass. "
                    f"Provide the full corrected content for each file."
                )
                generated_content = self._call_llm(correction_prompt, "You are an expert at fixing React/TypeScript tests.")
                self._write_dummy_files(file_paths, generated_content) # Overwrite with corrected code
                continue # Retry validation
            
            print(f"[AGENT] All checks passed for '{component_name}'!")
            return True, file_paths

        print(f"[AGENT] Failed to scaffold '{component_name}' after {self.max_retries} attempts.")
        return False, None

    def _extract_component_name(self, prompt):
        # Simple regex or LLM call to extract 'UserCard' from the prompt
        return "UserCard" 

    def _write_dummy_files(self, file_paths, content):
        # In a real setup, parse 'content' to get individual file contents
        # For this example, just create empty files or simple placeholders
        for key, path in file_paths.items():
            with open(path, "w") as f:
                # In real code, parse 'content' to get specific file's text
                f.write(f"// Generated {key} for UserCard\n") 
                f.write(content) # Not ideal, but for example purposes

# Example usage (assuming you have an OpenAI client configured)
# client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
# agent = FeatureScaffoldingAgent(client)
# success, paths = agent.scaffold_feature(
#    "Create a new React component named `UserCard` that displays `name` and `email` props. "
#    "Include a Jest test file and a Storybook story. Place it in `src/components/UserCard`"
# )
# if success:
#    print(f"Successfully scaffolded: {paths}")

Explanation of the Code Snippet:

• `FeatureScaffoldingAgent` orchestrates the process.
• `_call_llm` is a wrapper for your LLM API calls, including system messages for role-playing.
• `_run_command` executes shell commands (`eslint`, `jest`) and captures their output, which is crucial for feedback.
• The `scaffold_feature` method contains the core loop:
  • It first plans (though simplified in this demo to direct generation).
  • It then enters a `for attempt` loop for self-correction.
  • Inside the loop, it attempts to generate code (represented by `_write_dummy_files`).
  • It immediately runs `eslint` and `jest` as validation steps.
  • If either fails, it constructs a new prompt for the LLM, feeding back the error messages and the current code, asking for corrections. This is the heart of self-correction.
  • If all checks pass, it breaks the loop and reports success.

Real-World Example Walkthrough: Automated UserCard Creation

Imagine running the `scaffold_feature` method with our `UserCard` prompt. Here's a possible interaction flow:

1. Initial Generation: The agent generates `UserCard.tsx`, `UserCard.test.tsx`, and `UserCard.stories.tsx`. The LLM might initially forget to import `React` in the test file, or the Storybook configuration might have a typo.
2. Linter Check: The agent runs `eslint`. Let's say it finds a "React must be in scope" error in `UserCard.test.tsx` because `import React from 'react';` was missed. The agent captures this error.
3. Correction Loop 1 (Lint Fix): The orchestrator sends a new prompt to the LLM: "ESLint failed with 'React must be in scope for JSX' in `UserCard.test.tsx`. Here's the current code... Please fix." The LLM analyzes the error and the code, then regenerates the corrected `UserCard.test.tsx` (adding the import). The agent overwrites the old test file.
4. Test Check (Round 2): The agent re-runs `eslint` (which now passes) and then `jest`. This time, Jest might report "Expected `UserCard` to render 'email' but found nothing." This could mean the LLM initially hardcoded `email` instead of using the prop. The agent captures this test failure.
5. Correction Loop 2 (Test Fix): The orchestrator sends *another* prompt to the LLM: "Jest tests failed with 'Expected `UserCard` to render "email" but found nothing'. Here's the current code... Please fix the component and test if necessary." The LLM revises `UserCard.tsx` to correctly render the `email` prop and possibly adjusts the test to assert this. The agent overwrites the files again.
6. Final Validation: The agent re-runs `eslint` and `jest`. Both now pass.
7. Success! The agent reports success, and you have fully functional, linted, and tested `UserCard` files in your project, all generated and self-corrected autonomously.

This iterative feedback loop is what elevates a simple code generator to a truly intelligent, self-correcting agent. It minimizes the need for human intervention in the initial drafting and validation phases.

Outcomes and Takeaways

Implementing a self-correcting AI agent for feature scaffolding delivers significant benefits:

• Drastically Reduced Time-to-Feature: By automating the repetitive setup, developers can jump straight into implementing the unique logic of a feature.
• Consistent Code Quality: The agent ensures adherence to project standards, linting rules, and testing methodologies from the outset, reducing technical debt.
• Empowered Developers: Developers can focus on higher-level architectural decisions and complex problem-solving rather than mundane setup tasks.
• Living Documentation: The prompts and agent configurations can serve as a form of executable documentation for how features should be structured.
• Scalability: As your team or project grows, the agent scales your development capacity without proportional increases in setup overhead.
• Learning and Adaptation: Over time, by refining your prompts, tools, and potentially even training the LLM on your codebase, the agent can become increasingly sophisticated and tailored to your specific needs.

Conclusion: The Future of Autonomous Development

The concept of self-correcting AI agents isn't limited to feature scaffolding. Imagine agents that can refactor legacy code, debug complex issues by running diagnostics, or even set up entire cloud environments based on high-level specifications. We're on the cusp of a paradigm shift where developers move from writing every line of code to orchestrating intelligent systems that build and maintain it.

Building your first self-correcting agent might seem daunting, but starting small with a clearly defined, repetitive task like feature scaffolding is an excellent way to grasp the core concepts. The tools are mature, the LLMs are powerful, and the potential for increased productivity and job satisfaction is immense. It's time to stop just *using* AI and start *building* with it. Unleash your inner architect, and let your agents handle the blueprints.