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Autonomous AI Coding Agent

The Autonomous AI Coding Agent is designed to streamline the software development process by autonmously analyzing an exisiting local codebase, generating and implementing a feature into the codebase, and then creating a pull request on a given github repo with the implemented changes. This agent collaborates with a group of specialized agents to handle the workflow efficiently.

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Key Features

  • Autonomous Feature Development: Identifies potential improvements and new features for a codebase without user intervention.
  • Automated Workflow: Handles the end-to-end process, including coding, review, and implementation.
  • GitHub Integration: Creates pull requests directly on the specified GitHub repository.
  • Error Handling and Iterative Refinement: Continuously tests and improves the code until it meets quality standards.

Workflow Breakdown

Let's go over a workflow where an AI agent autonomously identifies potential new features, writes the necessary code, integrates it into the codebase, and submits a pull request to the GitHub repository.

A. Codebase Backup:

  • The user provides a GitHub access token and a github repository link. The AI creates a secure backup of the local codebase to ensure rollback options.

B. Feature Identification:

  • The Product Manager Agent analyzes the codebase and suggests potential new features.

C. Feature Implementation:

  • The Software Engineer Agent writes the code for the new feature.

D. Code Review:

  • The Code Reviewer Agent inspects the code for bugs, inefficiencies, and adherence to coding standards.

E. Feature Integration:

  • The Section Manager Agent integrates the new feature into the codebase.

F. Testing and Deployment:

  • Tests the implemented feature to ensure functionality.
  • Creates a pull request on the specified GitHub repository.

G. Backup Restoration:

  • Restores the original backup if errors arise during the process.

Example Use Case

User Query:

"Analyze this codebase and implement a feature that tracks user activity and logs it into a database."

Teams of Agents

A. Product Manager Agent:

  • Identifies the need for user activity tracking and creates a task description.

B. Software Engineer Agent:

  • Writes the Python code to track user activity and log it to a database.

C. Code Reviewer Agent:

  • Validates the code for potential bugs, inefficiencies, and adherence to best practices.

D. Section Manager Agent:

  • Integrates the new feature into the main codebase and creates a pull request.

Workflow

Step 1: Initial Query

  • User Query: "Create a Python script that scrapes data from a website, stores it in a CSV file, and optimizes the code for better performance."

  • User Input: Provides a GitHub access token and repository URL.

Step 2: Orchestrator Processing

  • The Orchestrator interprets the input and creates tasks:
  • Analyze the codebase for new features.
  • Implement the feature.
  • Review, test, and deploy the feature.

Step 3: Task Execution

A. Feature Analysis:

  • Product Manager Agent scans the codebase and identifies user activity tracking as a valuable addition.

B. Feature Implementation:

  • Software Engineer Agent writes a Python function to log user activity into a database or simulate such logging.

C. Code Review:

  • Code Reviewer Agent checks for vulnerabilities, syntax errors, and performance inefficiencies.

D. Integration:

  • Section Manager Agent integrates the feature into the codebase and creates a pull request.

Step 4: Testing and Iteration

  • Tests the feature for functionality and usability.
  • If issues arise, tasks are reassigned to relevant agents for debugging and improvement.

Step 5: Cycle Continues

  • The system iteratively improves the feature or script until it meets the desired performance and functionality standards.