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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. Powered by: NOFire AI Edge builds a causal graph of your infrastructure. This graph enables NOFire AI to analyze deployment impact and risk based on actual service dependencies and historical patterns.

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What You Get

Know What's Affected

See which production services your code changes touch. Understand the blast radius before you merge.

Understand the Risk

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

Right Deployment Strategy

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

See Potential Cascade

One service change or cascade to critical business functions? Understand downstream impact.

Historical Context

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
  • Canary rollout catches issue
  • 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 and get team-wide visibility during reviews.

In Dashboard

Review risks and dependencies in the web dashboard before deploying.

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Best Practices

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

## Before merging changes

Always check deployment risk with NOFire AI:

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
- Monitor for 15+ minutes between canary stages
See complete MCP integration guide →

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Getting Started

1

Install NOFire AI Edge

Deploy the Kubernetes agent that builds your causal graph. This is required for deployment risk assessment.Install NOFire AI Edge →
2

Connect Your IDE

Set up MCP integration to query NOFire AI from Cursor, Claude Desktop, or other MCP-compatible tools.Set up MCP integration →
3

Try Your First Risk Check

Make a code change, then ask: “What’s the deployment risk for these changes?”Review the risk score, affected services, and deployment strategy recommendation.

MCP Integration

Set up IDE integration for shift-left practices

API Tokens

Generate and manage MCP API tokens

NOFire AI Edge

Learn more about the foundation of causal intelligence

Security

Understand our security model

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Good to Know

NOFire AI learns your environment immediately after NOFire AI Edge connects. Risk assessments get more accurate as it observes deployment patterns and service interactions.
Brand new services get conservative risk assessments based on their architecture and dependencies. Accuracy improves after observing a few deployments.
NOFire AI analyzes your monitored infrastructure and causal graph. It can’t predict external service failures, third-party API issues, or manual operational mistakes.
The causal graph and risk models learn continuously. More usage means more accurate insights for your specific environment.