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Building Error Intelligence Infrastructure

December 20, 2024 • Chris Quinn

Summary

Error intelligence is infrastructure, not a product. Like feature flags sit between deployment and rollout, error intelligence sits between monitoring and debugging. We're building the platform that gives every AI coding assistant access to production error context, team patterns, and impact scores—whether they're using Claude, Copilot, Cursor, or tools that don't exist yet. This is the final piece that makes AI coding assistants production-aware.

We've spent ten posts unpacking the problem: AI coding assistants need error context to debug effectively, but monitoring tools like Sentry aren't built for AI consumption. The solution is an intelligence layer that enriches errors with patterns, context, and rankings, then exposes that data through APIs that AI tools can query.

This final post is about why we're building Cazon as infrastructure rather than an end-user product, and what that means for the future of AI-assisted development.

Infrastructure vs Product

Most developer tools are products. You sign up, use the interface, solve your problem. Sentry is a product: developers log in, view errors, investigate issues. GitHub is a product: developers push code, review PRs, manage issues.

Cazon is infrastructure. You integrate it once, and it powers other tools. The Cazon dashboard exists for configuration and team management, but the real value happens in the tools you already use: Claude Desktop queries errors via MCP, VS Code shows diagnostics and quick fixes, GitHub Copilot accesses team patterns through the chat participant.

This distinction matters because infrastructure scales differently than products:

Products optimize for UX. Infrastructure optimizes for APIs. Our public API (/api/v1/errors, /api/v1/patterns) is a first-class citizen, not an afterthought. AI tools need structured, queryable data, so we design for programmatic access first.

Products have one interface. Infrastructure has many. We provide an MCP server for Claude, a VS Code extension for editors, a REST API for custom integrations, and an SDK for error capture. Each interface exposes the same underlying intelligence in formats optimized for different tools.

Products lock you in. Infrastructure is portable. If you decide Cazon isn't working, your error data exports easily, and integrations disable cleanly. We're not trying to become your only debugging tool; we're trying to make all your debugging tools smarter.

Products compete. Infrastructure complements. We integrate with Sentry (webhooks), GitHub (issue import), Slack (notifications), and AI tools (MCP/API). The more tools you use Cazon with, the more valuable it becomes.

This is why we call it "error intelligence infrastructure" rather than "error debugging platform." It's a layer, not a destination.

The Three-Layer Stack

Here's how error tooling should work in the AI era:

┌─────────────────────────────────────────┐
│   AI Coding Assistants                  │ ← Claude, Copilot, Cursor
│   (Code generation & debugging)         │   Query errors, get suggestions
├─────────────────────────────────────────┤
│   Error Intelligence (Cazon)            │ ← Pattern matching, team context
│   (Enrichment, ranking, API exposure)   │   Expose via MCP, REST, VS Code
├─────────────────────────────────────────┤
│   Error Monitoring (Sentry, etc.)       │ ← Capture, aggregate, alert
│   (Capture production errors)           │   Send to intelligence layer
├─────────────────────────────────────────┤
│   Production Applications                │ ← Your code running
│   (Throw errors, log events)            │   SDK captures errors
└─────────────────────────────────────────┘

Each layer has a job:

Layer 1: Production Applications - Your code runs, errors happen, SDKs capture them Layer 2: Monitoring - Sentry/etc aggregate errors, alert humans, provide dashboards Layer 3: Intelligence - Cazon enriches errors with patterns, context, rankings Layer 4: AI Tools - Assistants query intelligence layer to help developers debug

The key insight: Layers 1-2 already exist and work well. Layer 4 is exploding (Claude, Copilot, Cursor, Aider, Continue). Layer 3 is missing, and without it, Layer 4 can't access Layer 2's data effectively.

Cazon is Layer 3. We don't replace Sentry; we sit on top of it. We don't replace Claude; we provide data for it.

Platform Strategy: Four Integration Points

To serve as infrastructure, we need to meet developers and AI tools where they already work. That's why we built four integration points:

1. SDK (Error Capture)

import { Cazon } from '@cazon/sdk';

const cazon = new Cazon({ apiKey: process.env.CAZON_API_KEY });

try {
  await riskyOperation();
} catch (error) {
  cazon.captureError(error);
}

The SDK captures errors from your application (Node.js, Python, browser) and sends them to Cazon for enrichment. This is the entry point: errors flow in from production code.

2. REST API (Programmatic Access)

curl https://cazon.dev/api/v1/errors \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"

The REST API lets any tool query enriched errors. Custom dashboards, CI/CD pipelines, Slack bots, or internal tools can all consume Cazon data programmatically.

3. MCP Server (AI Tool Integration)

{
  "mcpServers": {
    "cazon": {
      "command": "npx",
      "args": ["-y", "@cazon/mcp-server"],
      "env": {
        "CAZON_API_KEY": "your_key_here"
      }
    }
  }
}

The MCP server gives Claude Desktop (and eventually other MCP-compatible tools) direct access to your error intelligence. No copy-paste, no context switching—Claude queries Cazon directly.

4. VS Code Extension (In-Editor Intelligence)

VS Code extension provides diagnostics, quick fixes, and the chat participant. Errors appear as red squiggles with lightbulb fixes, and @cazon queries work in any VS Code chat interface.

Each integration point serves a different workflow. The SDK captures errors. The API powers custom tools. The MCP server enables AI assistants. The VS Code extension brings intelligence into the editor.

This is what platform strategy looks like: provide primitives (API, SDK, MCP), not prescribe workflows.

Network Effects: Better Patterns for Everyone

Most SaaS products have network effects within organizations (Slack is better when your team uses it), but Cazon has network effects across the entire developer community.

Here's how it works:

Phase 1: Team Intelligence - Your team uses Cazon, patterns emerge from your errors, team history accumulates, junior developers learn from senior developers' fixes.

Phase 2: Cross-Team Intelligence - Multiple teams in your organization use Cazon, patterns from one team help another team, systemic infrastructure issues become visible across projects.

Phase 3: Community Intelligence - Thousands of teams use Cazon, common error patterns (like "TypeScript null deference after auth check") are identified across codebases, pattern library grows from 353 signatures to thousands.

Phase 4: Ecosystem Intelligence - AI tools integrate with Cazon, pattern matches improve as more errors flow through the system, better patterns benefit everyone using those AI tools.

We're building toward Phase 4. The pattern library should eventually work like ESLint rules: community-contributed, open-sourced, constantly improving. When someone discovers a new error pattern, they can submit it to the library, and everyone benefits.

This only works if we're infrastructure. If we were a closed product, patterns would stay internal. As an open API platform, patterns become a shared resource.

The Revenue Model

Infrastructure companies monetize through platform adoption, not per-seat pricing. Cazon has three revenue streams:

1. B2C (Individual Developers)

Free tier for personal projects, paid plans for production use. Similar to Vercel or Supabase: developers start free, convert when they deploy to production or need team features.

Pricing based on errors processed per month (similar to Sentry), not seats. You pay for usage, not headcount.

2. B2B (Teams)

Team plans include organization features: shared error visibility, assignments, status tracking, team history, and priority overrides. Priced per organization, not per developer.

3. B2B (AI Tool Partnerships)

AI tool companies (like Cursor, Aider, Continue.dev) integrate Cazon's intelligence layer. They get better error context for their users, we get distribution and usage volume. Revenue share or licensing deals.

The third stream is the infrastructure play. If Cursor integrates Cazon and offers error intelligence to their 100K+ users, that's massive distribution without us building a competing code editor.

This is how infrastructure companies scale: enable others to build on your platform, take a percentage of the value created.

Future Integrations

The AI coding assistant landscape is exploding. In the past year we've seen:

  • Cursor - AI-first code editor
  • Aider - Terminal-based AI coding assistant
  • Continue.dev - Open-source Copilot alternative
  • Windsurf - Codeium's AI editor
  • Copilot Workspace - GitHub's holistic AI development environment

Each tool is trying to go beyond code generation to full-stack debugging, but none have production error context. They rely on users copy-pasting stack traces or manually describing errors.

Cazon solves this. We're actively working on integrations with:

Cursor: Custom context provider that lets Cursor query Cazon errors when debugging Continue.dev: MCP server support (already works, needs documentation) Aider: API integration for error-aware commit messages Windsurf: Similar to Cursor, leveraging their extensibility API

The goal is ubiquity: every AI coding assistant should be able to query Cazon for production error context. Developers shouldn't have to switch tools or manually transfer context.

The Core Bet

Everything we're building rests on one assumption: AI tools will dominate software development within 5 years, but they'll only be as good as the context they can access.

Right now, AI coding assistants are great at greenfield code generation. Give Claude a clear spec, and it writes working code. But debugging requires different context:

  • What errors are happening in production?
  • Which errors are critical vs noise?
  • Have we seen this error before?
  • How did we fix it last time?
  • What patterns does this error match?

None of this context exists in LLM training data. It's runtime data, specific to your codebase, constantly changing. AI tools need a live connection to this intelligence, not static training.

Cazon provides that connection. We're betting that as AI tools get better at code generation, the bottleneck shifts to context access. The team with the best production error intelligence wins.

If we're right, Cazon becomes essential infrastructure in every AI-assisted development workflow. If we're wrong, and AI tools don't need production context, then we've built a nice error debugging product that helps individual developers. Lower ceiling, but still valuable.

We're betting on the higher ceiling.

Why Now?

Three things changed in 2024 that make error intelligence infrastructure viable:

1. MCP Protocol - Anthropic released Model Context Protocol, creating a standard way for AI tools to query external data sources. Before MCP, every integration was custom. Now we build one MCP server and it works with any MCP-compatible tool.

2. VS Code Chat API - Microsoft shipped the Chat Participant API, letting extensions register @commands in Copilot Chat. This turns VS Code into a platform for AI tool integrations.

3. AI Coding Assistant Adoption - GitHub Copilot hit 1M+ paid seats, Cursor crossed 100K users, Claude Desktop became the default dev tool for many engineers. AI-assisted coding is mainstream now, not experimental.

These three shifts create the platform opportunity. MCP provides the integration standard, VS Code provides the distribution channel, and widespread adoption provides the market. Five years ago, this wouldn't have worked. Five years from now, it'll be too late (someone else will have built it).

The timing is now.

Founder Story: 15 Years of the Same Bugs

I've been writing production code for 15 years. React apps, Node APIs, Python microservices, TypeScript tooling. Different languages, different frameworks, different companies.

The bugs are always the same.

NULL pointer exceptions. Race conditions. Memory leaks. Off-by-one errors. Type mismatches. Missing error handling. Timeout configuration. Database connection pooling.

You fix them, they come back. Different files, different variable names, same root cause. Junior developers hit the same bugs senior developers fixed five years ago. Teams waste hours debugging issues that have known solutions.

The problem isn't that developers don't learn. The problem is that debugging knowledge doesn't accumulate. It lives in people's heads, disappears when they leave, and resets every time a new developer joins.

I started Cazon because I'm tired of fixing the same bugs. Error intelligence infrastructure means we finally build a system that learns, remembers, and shares solutions across teams, across projects, across time.

This is the tool I wish I'd had 15 years ago.

What's Next

This series covered the problem (random debugging), the context AI tools are missing (error intelligence), and the solution we're building (enrichment infrastructure with SDK, API, MCP, and VS Code integration).

The vision is clear: every AI coding assistant should have production error context. We're building the platform that makes that possible.

Try it today:

  • Main app: cazon.dev
  • VS Code Extension: Search "Cazon" in VS Code marketplace
  • MCP Server: npm install -g @cazon/mcp-server
  • Documentation: cazon.dev/docs

We're in beta now, onboarding teams weekly. If you're building AI development tools and want to integrate error intelligence, reach out: chris@cazon.dev

Let's make debugging less random, together.

This is the final post in our launch series on error intelligence. Thanks for following along. Now go ship something.

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