intel.layer.ai

Your bug tracker has 47 open errors, but which one is breaking checkout? Which affects just one user? Which is getting worse this week? Most teams debug randomly instead of strategically, wasting time on low-impact bugs while critical issues affect revenue. Without impact scoring and trend analysis, bug backlogs become undifferentiated noise where revenue-critical errors sit alongside cosmetic annoyances.

Claude, Copilot, and Cursor are exceptional at writing code but terrible at debugging because they lack production context. They have no impact scores, no trend data, no team history, and no memory of what worked last time. You're giving a brilliant junior engineer the keys to your codebase without an engineering manager to guide priorities.

Error Intelligence is the layer between production monitoring (Sentry) and AI coding tools (Copilot). It's just not about capturing errors (Sentry does that) or generating fixes (AI does that); it's about enriching errors with impact scores, pattern matching, team history, and structured context that AI tools can consume. Think of it as engineering manager perspective, delivered as API.

Cazon doesn't force you to replace your existing error monitoring. Install the Node.js SDK for automatic capture in production, or connect your Sentry account via webhook to enrich errors you're already collecting. Either way, errors flow into Cazon for analysis, pattern matching, and impact scoring, then get exposed to your AI tools.

When a "Cannot read property of undefined" error hits your API, Cazon matches it against a curated library covering thousands of common React, JavaScript, TypeScript, and build tool errors. You get instant context: what causes this, common fixes, severity, and whether it's a rookie mistake or a framework bug. Pattern matching runs before AI analysis, providing instant feedback like Stack Overflow patterns as code.

We're building an ML model that trains on YOUR organization's GitHub issue history. By analyzing how you've labeled, prioritized, and resolved past issues, the model learns which error patterns matter most to your team. SaaS companies might prioritize issues affecting many users, internal tool teams might prioritize severity, and B2B platforms might prioritize customer-facing errors. The model adapts to your domain automatically by learning from your historical GitHub data; no configuration needed. This feature is in beta testing now—reach out if you want early access.

The Model Context Protocol (MCP) is Anthropic's open standard that lets AI assistants access external data sources. Cazon's MCP server connects Claude Desktop to your production errors, enriched with pattern matching and impact scores. Instead of copying stack traces into chat, Claude can query your error database directly, understand which errors matter most, and suggest fixes based on your team's patterns. One config file change, and your AI assistant becomes production-aware.

The Cazon VS Code extension brings production error intelligence directly into your editor. Errors captured by the Cazon SDK appear as red squiggles in your code with pattern-matched suggestions and AI-powered fixes available through lightbulb quick actions. The extension includes a built-in MCP server that automatically exposes your error data to Claude Desktop and other AI assistants, plus a public API that lets GitHub Copilot and Cursor query your error context without leaving the editor. Install once, debug everywhere.

When a senior developer fixes a production error, that knowledge should benefit the entire team. Cazon makes error intelligence team-wide by design: all errors are visible to your organization, fixes can be documented and shared, and historical context shows how similar errors were resolved before. Junior developers learn from senior developers' past fixes without interrupting them, and patterns discovered in one project help debug issues in another. This is how debugging knowledge scales.

Sentry captures errors for humans to view in dashboards. Cazon enriches those same errors with intelligence for AI tools to query via API. Different output, different customer, different job. We integrate with Sentry through webhooks—they capture, we enrich, AI tools consume. This post explains why we're complementary, not competitive.

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.