Tinkerers of
Computer Engineering Department VESIT
CodeCell
++
SYRUS HACKATHON 2026
SYRUS HACKATHON 2026
CODECELL++ CMPN VESIT
TRACK:
PS TITLE:
TEAM NAME:
[Track 1: Agentic AI [Rezinix AI]]
[Autonomous Incident-to-Fix Engineering Agent]
[CtrlAltElite]
Modern software systems rely on platforms such as GitHub, CI/CD pipelines, and issue tracking tools to manage production systems. When a bug or failure occurs, engineers must manually go through multiple steps including reading incident tickets, analyzing logs, debugging code, researching documentation, writing fixes, and validating the solution.
This process is time-consuming, repetitive, and does not scale well with the increasing complexity of modern software systems. As development cycles become faster, manual incident resolution becomes a major bottleneck.
Our goal is to design an Autonomous Incident-to-Fix Engineering Agent that can automatically interpret incident reports, analyze the codebase, identify root causes, apply fixes, validate them through tests, and generate a structured resolution report with minimal human intervention.
Problem Understanding
We propose an Agentic Engineering Platform that automates the entire software incident resolution lifecycle.
The system accepts natural language incident tickets and intelligently interprets the failure context using an AI agent. The platform then analyzes the project codebase to detect potential causes such as logical errors, dependency issues, or configuration problems.
Once the root cause is identified, the agent automatically generates minimal fixes and applies them to the affected files. The system then executes tests in a sandboxed environment to ensure correctness and prevent regressions.
Finally, the platform generates a structured resolution report summarizing the root cause, changes applied, validation results, and confidence score.The platform also provides explainable debugging by generating a clear explanation of the root cause and the changes applied to resolve the issue.
This approach significantly reduces manual debugging effort and accelerates the incident resolution workflow for engineering teams.
When the root cause is unclear, the system retrieves relevant documentation, error references, and best practices to ensure accurate and reliable fixes.
Introduction / Proposed Solution
Architecture Diagram / Tech stack
Architecture Flow:
Incident Ticket
↓
Ticket Understanding Agent
↓
Codebase Analyzer
↓
Root Cause Detector
↓
Fix Generator
↓
Sandbox Execution Environment
↓
Test Runner
↓
Confidence & Risk Scoring
↓
Resolution Report
Tech Stack:
Programming Language: Python
AI Engine: LLM-based reasoning agent
Code Analysis: Python AST / Static Analysis
Sandbox Execution: Python
Subprocess / Container Environment
Testing Framework: PyTest
Version Control: GitHub Repository
User Interface : CLI or lightweight dashboard
Our solution introduces an autonomous debugging pipeline that bridges the gap between incident reporting and production-ready fixes.
Key innovations include:
• AI-powered understanding of natural language incident tickets
• Intelligent root cause detection through repository analysis
• Autonomous generation of minimal code fixes
• Sandbox-based validation with automated testing
• AI-driven confidence and risk scoring before applying fixes
• Explainable debugging with structured resolution reports
This system enables engineering teams to significantly reduce manual debugging time and accelerate the development lifecycle.
End-to-end autonomous debugging pipeline from incident ticket to verified fix.
Novelty / USP / Showstopper
Phase 1 — Incident Understanding
The agent parses natural language incident tickets and extracts key information such as error type, affected components, and environment context.
Phase 2 — Codebase Analysis
The system scans the repository to identify relevant modules, dependencies, and potential failure points.
Phase 3 — Root Cause Identification
Using static analysis and AI reasoning, the platform identifies the most probable cause of the incident.
Phase 4 — Autonomous Fix Generation
The agent generates minimal code patches and applies fixes to the affected files while maintaining system stability.
Phase 5 — Validation & Testing
The system executes the application and test cases in a sandboxed environment to ensure correctness.
Phase 6 — Resolution Reporting
A detailed report is generated summarizing the root cause, changes applied, validation results, and confidence score.
Implementation Plan
Impact:
• Reduces manual debugging effort
• Accelerates incident resolution
• Improves reliability of production systems
• Assists DevOps and SRE teams in handling failures efficiently
Future Enhancements:
• Integration with Slack and Jira for automatic incident ingestion
• Automatic GitHub branch creation and Pull Request generation
• Advanced risk scoring before applying fixes
• Support for multiple programming languages and frameworks
iMPACT / FUTURE WORK