🧠 Code Evaluation Orchestra
AI Agents Assemble — Hackathon Submission
🔗 Live Demo: https://mecode-evaluation-orchestra.vercel.app/
🚀 Overview
Code Evaluation Orchestra is an AI-agent-inspired coding practice and interview evaluation platform.
Users choose a problem, write a solution, and receive structured, interview-style feedback instead of only pass/fail results.
The project simulates how multiple AI agents collaborate to evaluate code — similar to how real interviewers assess solutions.
❓ Problem Statement
Learners often struggle to understand:
-What logic was missing
-Which edge cases failed
-Whether their solution is interview-ready
Code Evaluation Orchestra addresses this gap by emphasizing clarity, feedback, and explanation.
The system is designed as a collaborative multi-agent workflow:
| Agent | Responsibility |
|---|---|
| Test Generator | Provides problem-specific test cases |
| Code Analyzer | Inspects solution logic patterns |
| Evaluator | Assigns a score based on correctness |
| Summarizer | Generates interview-style feedback |
Together, these agents produce a single, clear, and understandable evaluation for the user.
✨ Key Features
-
Multiple Coding Problems
A curated set of coding challenges across different difficulty levels. -
Clear Problem Statements
Each problem includes a concise and well-defined description. -
Interactive Code Editor
Users can write and edit solutions directly in the browser. -
Starter Code Templates
Predefined function signatures to guide candidates. -
Problem-Specific Test Cases
Each problem includes visible test cases. -
Scoring System
Solutions are scored based on correctness and quality. -
Technical Analysis
Explains why a solution passes or fails. -
Interview-Style Feedback
Human-readable feedback similar to real interviews.
🧪Example User Flow:
Select a coding problem
Read the problem statement
Write a solution
Click Run Evaluation
View score, test results, analysis, and summary
🧩 Sponsor Tool Usage
-⚡ Stormbreaker Deployment (Vercel)
Used for fast and reliable deployment.
🏁 Results & Impact
Improves learning by explaining why a solution works or fails
Mimics real interview evaluation
Encourages better problem-solving habits
👩💻 Author
Dimple Goyal
GitHub: https://github.com/me-Dimple72
🗣 Interview-style summary feedback