Why AI Tools Can't Prioritize Your Bugs
Summary
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.
AI coding assistants have transformed how we write code. Claude, Copilot, and Cursor are extraordinary tools that can write a React component, refactor a function, or implement an algorithm in seconds. But ask them to prioritize which production bug to fix first, and they're completely useless.
The Copy-Paste Stack Trace Problem
Here's the typical workflow when a production error appears:
- Check monitoring tool (Sentry, New Relic, etc.)
- Copy the stack trace
- Paste into Claude/Copilot
- Ask: "What's causing this error?"
- Get a detailed explanation and suggested fix
Sounds efficient, right? But here's what's wrong with this workflow: the AI has zero context about whether this error matters. We copied one stack trace, but what if there are 47 other errors happening right now? What if this error affects 2 users while another error is breaking checkout for 200 users? What if this same error pattern was fixed last month but regressed? What if this error is trending down because we already deployed a partial fix?
The AI doesn't know any of this; it can only analyze the code we show it. It's like asking a junior developer to fix a bug without telling them about the other 46 bugs, the business impact, the user complaints, or the team's debugging history.
What AI Assistants Are Missing
Let's be specific about what production context AI tools lack:
- Impact Scores: How many users are affected? Is this blocking revenue or just annoying?
- Trend Analysis: Is this error getting better or worse? Was it fixed before and regressed?
- Team History: Did someone already investigate this? What did we try? What worked last time we saw this pattern?
- Error Frequency: Is this happening once per hour or 1,000 times per hour?
- User Context: Are premium customers affected? Is this breaking checkout or a rarely-used admin panel?
- Known Patterns: Do we have a documented fix for this class of error? Is this a database timeout we've seen 50 times?
Traditional AI assistants have none of this. They're trained on how to write code, not how to triage production issues; they can read your codebase, but they can't read your monitoring dashboards, your error logs, your team's Slack history, or your previous incident reports.
The Brilliant Junior Developer Without Guidance
Here's the best analogy: AI coding assistants today are like having 10 brilliant junior developers but no engineering manager. These junior devs are incredibly fast and can implement features, write tests, and refactor code faster than humans, but when production breaks, they don't know what to prioritize. They'll happily spend 2 hours fixing a typo in an error message while the payment API is throwing 500s.
Why? Because they lack the context that comes from monitoring dashboards showing which errors affect the most users, historical data showing which bugs tend to be critical, team knowledge about which systems are fragile, business priorities about which features generate revenue, and trend analysis showing which problems are escalating.
An engineering manager provides this context and can say: "Ignore that typo. The payment API is affecting 200 users and costing us $1,000/hour. Fix that first." AI assistants can't do this without error intelligence to guide them.
Why "Just Use Better Prompts" Doesn't Scale
Some might argue: "Just give the AI more context in your prompt. Copy multiple stack traces. Explain the business impact."
This doesn't scale. Here's why:
- Time Cost: Gathering context for 5-10 errors takes 30-60 minutes. We need to check logs, correlate user IDs, review trends, check previous fixes.
- Incomplete Data: We can't manually copy everything. We'll miss errors we don't know about, forget to mention regressions, and overlook patterns.
- Cognitive Load: Managing context across multiple chat sessions is exhausting; AI conversations reset and we lose continuity between debugging sessions.
- No Automation: Manual context gathering can't trigger automatically when new errors appear, so we're always reactive, never proactive.
The problem isn't prompt engineering. The problem is that AI assistants aren't connected to production intelligence and are isolated from the systems that know which errors matter.
What AI Needs (But Doesn't Have)
Imagine if AI coding assistants could access:
- Real-time error frequency and trend data
- Impact scores showing affected user counts
- Historical context about previous fixes
- Pattern matching against known error signatures
- Team notes about fragile systems and common pitfalls
- Business priority flags (e.g., "checkout errors are critical")
Now the AI could answer: "This error affects 200 users and is trending up 40% this hour. It's in the payment flow. We've seen this pattern before; last time it was a timeout issue resolved by increasing the connection pool. Priority: High."
That's error intelligence. Not just stack traces, but structured context about impact, trends, patterns, and history. AI assistants need this layer to be useful for production debugging.
The Missing Layer Between Monitoring and AI
We have monitoring tools (Sentry, New Relic, Datadog) and we have AI coding assistants (Claude, Copilot, Cursor), but there's a gap. Monitoring tools capture errors but don't provide intelligence; they're dashboards, not assistants. AI tools provide intelligence but don't have access to production data; they're isolated from reality.
The missing layer is error intelligence: a system that sits between monitoring and AI, providing automated impact scoring and trend analysis, pattern matching against historical errors, context-aware prioritization, and structured data AI assistants can consume.
This is what we're building at Cazon: not another monitoring tool or AI assistant, but an intelligence layer that connects production errors to AI-powered debugging.
What's Next
If you're a senior engineer or engineering manager, you've felt this gap. You've copied stack traces into Claude and wished it knew which error to prioritize; you've watched AI assistants generate brilliant fixes for low-impact bugs while critical issues went unfixed.
AI is incredible at fixing bugs once we tell it what to fix, but it can't tell us what to fix first. That requires error intelligence: impact scores, trends, patterns, and context.
Next: We'll define Error Intelligence and show how it bridges the gap between monitoring and AI.
This is Post 2 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.