What Is Error Intelligence?
Summary
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.
We've established the problem: teams debug randomly without impact context, and AI assistants can't prioritize bugs because they lack production intelligence. The solution isn't another monitoring tool or another AI assistant. The solution is a new layer that sits between them, providing the structured intelligence both humans and AI need to make better debugging decisions.
We call this Error Intelligence.
The Three-Layer Architecture
Modern development infrastructure has three distinct layers when it comes to debugging:
Layer 1: Monitoring (Capture)
Tools like Sentry, New Relic, and Datadog capture production errors. They collect stack traces, log messages, and metadata. They're excellent at answering: "What errors are happening?" But they stop there. They don't tell you which errors matter most, which patterns you've seen before, or what context AI tools need to fix them.
Layer 2: Intelligence (Enrich)
This is the missing layer. Error Intelligence takes captured errors and enriches them with impact scores (how many users affected?), pattern matching (have we seen this before?), trend analysis (is this getting worse?), team history (what did we try last time?), and structured context that AI assistants can consume. It answers: "Which errors should we fix first, and what context do we need?"
Layer 3: AI Tools (Fix)
Claude, Copilot, Cursor, and VS Code extensions generate fixes once they know what to fix. They're brilliant at code generation but blind to production priorities. They need Layer 2 to tell them where to focus.
Most teams have Layer 1 and Layer 3 but are missing Layer 2. They manually bridge the gap by copying stack traces from monitoring tools into AI chat windows, losing context along the way.
What Enrichment Actually Means
When we talk about "enriching" errors, we mean adding four types of intelligence:
Pattern Matching
Every error has a signature based on its stack trace, error message, and context. By fingerprinting errors, we can identify when a new error matches a known pattern. If your team has seen TypeError: Cannot read property 'user' of undefined 47 times in the past year, and you have documented fixes, that context should be instantly available. Pattern matching turns "new error" into "known problem with known solution."
Impact Scoring
Not all errors are equal. Some affect 1 user, some affect 1,000 users. Some break checkout, some misalign a button. Impact scoring uses error frequency, affected user counts, error location (critical vs non-critical code paths), and business priority flags to assign numerical scores. Now AI tools can prioritize: "This error affects 200 users in the payment flow; that error affects 1 user in the admin panel."
Trend Analysis
Errors change over time. An error might spike from 10/hour to 100/hour, indicating a regression. Or it might trend downward because a partial fix was deployed. Trend analysis tracks error frequency over hours, days, and weeks, highlighting which problems are escalating and which are resolving naturally. This prevents teams from wasting time on errors that are already improving.
Team Context
Human debugging history matters. If someone investigated an error last week and documented that it was a transient database timeout resolved by retrying, that knowledge should persist. Team context includes previous investigation notes, attempted fixes, related errors, and tribal knowledge captured as structured data. It's like having a senior engineer who remembers every bug the team has debugged.
Why This Is Infrastructure, Not a Product
Error Intelligence is infrastructure in the same way feature flag platforms (LaunchDarkly), observability tools (Datadog), or incident management systems (PagerDuty) are infrastructure. It's not a standalone product you "use" directly; it's a layer that powers other tools.
Consider feature flags: you don't log into LaunchDarkly to manually toggle flags all day. Instead, you integrate it into your deployment pipeline, your analytics tools, your experimentation platform. It's infrastructure that makes other workflows possible.
Error Intelligence works the same way. You don't log into Cazon to stare at error dashboards; you integrate it with your AI coding assistant, your VS Code extension, your CI/CD pipeline. It enriches errors once and exposes that intelligence to every tool that needs it through APIs, SDKs, and protocols like MCP.
This is why we built Cazon as infrastructure, not as another monitoring dashboard. We're not replacing Sentry or competing with Claude. We're filling the gap between them, providing the structured intelligence layer that neither can provide alone.
The Key Insight: AI Needs Structured Data
Dashboards are built for humans. They have charts, graphs, color-coded severity levels, and filtering UIs. But AI tools don't browse dashboards. They consume APIs.
The breakthrough insight behind Error Intelligence is that AI assistants need machine-readable, structured context about production errors. Instead of showing a dashboard that humans interpret manually, we expose error intelligence through APIs that AI tools can query programmatically.
For example, instead of a human looking at a Sentry dashboard, deciding which error is critical, copying the stack trace, and pasting it into Claude, the workflow becomes:
- Production error occurs
- Cazon captures and enriches it (impact score, pattern match, trend)
- VS Code extension queries Cazon's API: "What's the highest priority error right now?"
- Cazon responds with structured JSON: error details, affected users, known patterns, suggested priority
- AI assistant receives this context automatically and generates a fix
This eliminates the manual copy-paste workflow and ensures AI tools always have the context they need. It's infrastructure designed for machine consumption, not human dashboards.
Comparison to Other Intelligence Layers
Error Intelligence isn't a new concept; it's applying lessons from other domains:
Feature Flags separate deployment from release. Instead of deploying code and hoping it works, you deploy behind flags and control exposure. This is an intelligence layer between code and production.
A/B Testing Platforms separate experiments from implementation. Instead of manually tracking metrics, you define experiments once and the platform handles data collection and statistical analysis. This is an intelligence layer between product changes and user data.
Observability Tools separate metrics collection from alerts. Instead of manually checking logs, you define SLOs and the platform alerts when thresholds are breached. This is an intelligence layer between system health and incident response.
Error Intelligence separates error capture from debugging decisions. Instead of manually triaging errors, you define impact rules once and the platform scores, patterns, and prioritizes automatically. This is an intelligence layer between production errors and AI-powered fixes.
Each of these layers solves the same problem: turning raw data into actionable intelligence that machines can consume.
What This Means in Practice
Here's what changes when you have Error Intelligence:
Before: Check Sentry, see 47 errors, manually assess each one, guess which matters, copy stack trace into Claude, hope the fix works.
After: Open VS Code, Cazon extension shows top 3 errors sorted by impact, click one, AI assistant already has full context (affected users, known patterns, team history), generates fix in seconds.
Before: Junior developer asks "which bug should I fix?" Senior developer spends 15 minutes explaining business context, checking logs, researching history.
After: Junior developer queries Cazon API, gets structured priority list with impact scores and context, makes informed decision independently.
Before: AI assistant generates fix for low-impact bug because that's the one you happened to mention first. Critical bugs go unfixed because you didn't know they existed.
After: AI assistant surfaces high-impact bugs proactively, prioritizes based on business impact, suggests fixes for problems you didn't know about yet.
This is the shift from reactive debugging to intelligence-driven debugging.
What's Next
Error Intelligence is the foundation. It's the layer that makes everything else possible: smarter AI assistants, better developer tools, automated triage, and faster incident response. But foundation infrastructure is only valuable if it connects to the systems you already use.
Next, we'll show you how Cazon captures production errors from your existing stack (JavaScript SDK, Sentry integration) and starts building the intelligence layer for your team.
This is Post 3 of our launch series on error intelligence. Follow along as we unpack the problem, introduce solutions, and show you how Cazon is building the missing intelligence layer between production errors and AI coding assistants.