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Overview

Traditional approach: Deploy and hope nothing breaks. Find out in production. Proactive approach: Know the risk before you deploy. Prevent incidents before they happen.

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What You Get

Know What's Affected

See which production services your code changes touch. No surprises about what you’re impacting.

Understand the Risk

Get a clear assessment: Low, Medium, or High risk. Know if you can deploy now or need extra precautions.

Right Deployment Strategy

Get specific guidance: standard deploy, canary rollout, or staged deployment with team present.

See Potential Cascade

Understand what else could be affected. One service change or cascade to critical business functions?

Historical Context

Get warnings if a service had recent incidents or rollbacks. Don’t repeat last week’s mistakes.

IDE Integration

Check risk while coding in Cursor or Claude Desktop. No context switching required.

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Real-World Impact

  • Payment Service
  • Large Refactoring
  • Daily Development

Without NOFire AI

  • Friday afternoon deploy ❌
  • Payments fail 10 min later
  • 15 services cascading failure
  • 2 hours incident response
  • Revenue impact + angry customers

With NOFire AI

  • HIGH RISK warning before merge ✅
  • Recent instability alert shown
  • Deploy rescheduled to Tuesday
  • Staging catches the issue
  • Canary rollout with fix
  • Zero production impact

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How to Use It

In Your IDE

Check risk while coding in Cursor or Claude Desktop. Ask: “What’s the deployment risk?”

In Slack

Ask @NOFireAI bot about deployment risk, investigate incidents, and query production collaboratively with your team.

In Dashboard

Review risks and dependencies in the web dashboard before deploying.

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Best Practices

  • Automate It
  • Make It Routine
  • Team Practices
Add NOFire AI to your AGENTS.md so AI coding agents automatically check risk:
# AGENTS.md

## Before merging changes

Always check deployment risk with NOFire AI before merging:

1. Run tests: `npm test` or `pytest`
2. Ask: "What's the deployment risk for these changes?"
3. Follow the recommended deployment strategy:
   - HIGH RISK: staging first, then canary (5% → 25% → 100%)
   - MEDIUM RISK: canary deployment (10% → 50% → 100%)
   - LOW RISK: standard deployment

## Deployment guidelines

- Never deploy high-risk changes on Fridays or before holidays
- Deploy high-risk changes during business hours with team available
- Notify relevant teams before deploying critical services
- Monitor for 15+ minutes between canary stages
Works with Cursor, Claude Desktop, GitHub Copilot, and other AI coding agents.See complete AGENTS.md guide →

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Getting Started

Quickstart

Connect your data and get NOFire AI running in minutes

MCP Integration

Set up IDE integration for shift-left reliability

API Tokens

Generate and manage MCP API tokens

Security

Understand our security-first approach

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Good to Know

NOFire AI learns your environment immediately after connecting your data sources. Risk assessments get more accurate as it observes your production environment.
Brand new services get conservative risk assessments based on their architecture. Accuracy improves after a few deployments.
NOFire AI analyzes your monitored infrastructure. It can’t predict external service failures or manual operational mistakes.
The system learns continuously from your environment. More usage = more accurate insights for your specific setup.

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What’s Next?

MCP Integration

Set up IDE integration for shift-left practices

API Tokens

Learn how to generate and manage MCP API tokens

Security

Understand our security model

FAQ

Common questions and answers