IncidentPilot: AI-Powered Incident Response Agent

From Alerts to Answers: Making Incident Response Smarter with AI

Inspiration

In today's digital world, even a few minutes of application downtime can disrupt thousands of users. When production systems fail, developers and DevOps engineers must manually analyze logs, investigate errors, identify root causes, and determine appropriate recovery actions.

Traditional monitoring tools generate alerts, but understanding why something went wrong often requires significant manual effort.

We asked ourselves:

What if AI could act as an intelligent incident investigation assistant, helping developers understand system failures and identify possible solutions within seconds?

This inspired us to build IncidentPilot, an AI-powered incident investigation and response platform designed to make system troubleshooting more intelligent, efficient, and accessible.

Our vision is to move beyond traditional monitoring toward AI-assisted incident analysis, evidence-based recommendations, and human-controlled remediation.

What It Does

IncidentPilot combines an interactive incident management dashboard with an AI-powered investigation backend.

1. Centralized Incident Dashboard

Our dashboard provides a unified interface for viewing and managing system incidents.

Users can explore:

• Incident details and severity levels
• Affected services and system status
• Application logs and error messages
• Incident investigation progress
• AI-generated findings and recommendations

2. AI-Powered Root Cause Analysis

IncidentPilot uses a LangGraph-based AI investigation workflow to process incident information and analyze possible causes.

The system combines deterministic checks with language model reasoning to generate structured investigation reports.

3. Intelligent Log Analysis

IncidentPilot supports analyzing application log data to identify patterns, errors, and potentially relevant evidence.

This helps transform complex technical information into more understandable findings.

4. AI-Generated Recommendations

The AI investigation process generates potential root causes and troubleshooting recommendations.

Rather than providing only an alert, IncidentPilot aims to explain what might have happened and suggest appropriate next steps.

5. Human-in-the-Loop Remediation

IncidentPilot includes an approval workflow for recommended remediation actions.

The current prototype simulates approved actions rather than modifying production infrastructure.

This demonstrates how AI-assisted incident response can preserve human oversight.

6. Evidence-Based Investigation

The backend includes mechanisms for validating evidence references and handling untrusted log content.

This helps make AI-generated incident reports more structured and accountable.

How We Built It

IncidentPilot uses a modular full-stack architecture that separates the user interface, API layer, AI investigation workflow, and model inference provider.

Frontend - Next.js, React, TypeScript, Tailwind CSS

We developed an interactive incident management dashboard using:

• Next.js for the frontend framework
• React for reusable UI components
• TypeScript for type-safe application development
• Tailwind CSS for responsive styling

The dashboard includes incident views, service information, log visualization, investigation reports, and remediation approval controls.

Backend - Python and FastAPI

We used Python and FastAPI to build the backend services.

The backend provides REST API endpoints for:

• Retrieving incidents
• Accessing incident details and logs
• Starting AI investigations
• Retrieving investigation results
• Recording simulated remediation approvals

The frontend communicates with these endpoints to display incident information and investigation results.

AI Agent - LangGraph

We used LangGraph to organize the AI investigation workflow.

The investigation architecture combines:

1. Incident and log ingestion
2. Evidence collection
3. Deterministic analysis
4. LLM-based reasoning
5. Structured report generation
6. Evidence validation
7. Remediation recommendations

This modular approach makes the AI investigation process easier to extend and maintain.

NVIDIA AI - Nemotron

The backend includes configuration for NVIDIA Nemotron, an open-source model family designed for advanced language understanding and reasoning.

Our architecture is designed to use Nemotron for interpreting incident evidence, identifying possible root causes, and generating troubleshooting recommendations.

Nebius - Token Factory

IncidentPilot includes an integration path for Nebius Token Factory through an OpenAI-compatible API.

The backend can be configured to send inference requests to Nebius using API credentials and a supported model.

For local development, the application also supports Ollama, allowing the investigation workflow to run with a locally hosted language model.

Frontend-Backend Integration

We combined the frontend dashboard and AI backend into a unified project structure.

The integration connects the dashboard to FastAPI endpoints while preserving the original frontend layout and design.

The application also retains mock data support for demonstrating the interface independently of the live backend.

Challenges We Faced

1. Integrating Two Different Codebases

One of our biggest challenges was connecting an existing Next.js dashboard with a separate Python AI backend.

The frontend and backend originally used different API structures, so we needed to create compatible endpoints and adapt the data flow without redesigning the interface.

2. Handling Complex Incident Data

Application logs contain large amounts of technical information, including irrelevant messages, repeated errors, and potentially sensitive data.

We worked on structuring the investigation pipeline to process incident evidence more effectively.

3. Making AI Recommendations Reliable

Large language models can generate plausible explanations that are not always supported by the available evidence.

To address this, we incorporated evidence-reference validation and structured investigation outputs.

4. Protecting the AI Investigation Process

Incident logs may contain sensitive information or untrusted instructions.

Our backend incorporates safeguards such as sensitive-data redaction and prompt-injection-aware processing.

5. Balancing Automation with Human Control

Automatically executing AI-generated recovery actions can introduce operational risks.

We designed an approval-based remediation workflow and kept recovery actions simulated in the prototype.

What We Learned

Developing IncidentPilot gave us practical experience with:

• Full-stack development using Next.js and FastAPI
• Building and integrating REST APIs
• Designing agentic AI workflows with LangGraph
• Understanding NVIDIA Nemotron and Nebius inference integration
• Processing technical logs for AI analysis
• Structuring LLM-generated investigation reports
• Designing safer human-in-the-loop AI systems
• Integrating separate frontend and backend codebases

Most importantly, we learned that useful AI systems require more than a powerful language model. They also need reliable data handling, clear workflows, meaningful outputs, and appropriate human oversight.

What's Next?

We plan to extend IncidentPilot with:

• Real-time monitoring integrations
• Automated incident correlation
• Historical incident analysis
• Persistent incident storage
• Slack and email notifications
• Predictive incident detection
• More advanced NVIDIA AI integration
• Scalable deployment using Nebius infrastructure
• Approval-based integration with production remediation tools

Our Vision

IncidentPilot represents our vision of a smarter incident response workflow where AI helps engineers investigate failures, understand technical evidence, and make informed recovery decisions.

We aim to reduce repetitive troubleshooting work and help engineering teams build more reliable applications.

IncidentPilot - From Alerts to Answers.

Built With

• artificial-intelligence
• fastapi
• generative-ai
• github
• human-in-the-loop
• incident-management
• javascript
• langchain
• langgraph
• large-language-models
• nebius
• nebius-token-factory
• nemotron
• nextjs
• nvidia
• openai-api
• prompt-engineering
• pydantic
• python
• react
• rest-api
• tailwindcss
• typescript