Why Claude Code Fails: AI Tools That Actually Work for Engineering Teams
Claude writes beautiful code. So does Copilot. ChatGPT can spin up a React component faster than you can say "best practices."
But ask any of them to refactor your authentication flow across twelve services, and you get confident nonsense.
The problem isn't the AI. It's the context window.
The Context Window Problem Nobody Talks About
Here's what happens when you ask Claude to "add rate limiting to our API":
You paste in the relevant files. Claude generates code. It looks good. You commit it.
Two weeks later, someone notices you're now rate limiting per request instead of per user. Your database service has its own rate limiter that conflicts with the new one. The error messages don't match the format your frontend expects.
Claude didn't fail because it's bad at coding. It failed because it saw three files when it needed to understand thirty.
Real codebases have dependencies that span directories. Business logic that touches five different services. Patterns that evolved over years. A single feature often involves:
The actual implementation files
Database migrations
Configuration files
Tests that validate behavior
Documentation that explains the why
Related services that consume the API
Error handling that propagates up the stack
No developer holds all of this in their head. We navigate codebases by following connections. We grep for function calls. We check git blame to understand why something exists.
AI tools can't do this. They only see what you explicitly paste.
Why "Just Add More Context" Doesn't Work
The obvious solution seems simple: give the AI more files.
This breaks down fast.
First, there's the token limit. Claude's 200K context window sounds huge until you're dealing with a real codebase. A medium-sized service can easily be 50K lines of code. That's before you add tests, configs, and related services.
Second, more context doesn't mean better context. Dumping fifty files into Claude's context window is like handing someone a phone book when they need a contact. The AI spends tokens parsing irrelevant code instead of focusing on what matters.
Third, you don't know what's relevant until you've already done the work. To gather the right context, you need to understand:
Which services call this function
Where this pattern is used elsewhere
What broke the last time someone changed this
Who owns the related code
What the original design intention was
If you already know all that, you probably don't need AI help.
What Actually Works: Code Intelligence + AI
The solution isn't better prompts or bigger context windows. It's giving AI tools access to actual codebase knowledge.
This is where code intelligence platforms come in. Tools like Glue automatically index your entire codebase—not just the code, but the relationships between components, the patterns you use, ownership information, and how things have changed over time.
When you ask an AI tool to make a change, instead of guessing which files to include, you query the knowledge graph. It tells you:
All the places this function is called
Similar implementations in other services
Recent changes to related code
Team ownership and who to review with
Complexity and churn metrics (will this change bite you?)
Now your AI tool works with actual context instead of random files you remembered to include.
Here's a concrete example. You want to add pagination to an API endpoint.
Without code intelligence:
You paste the endpoint handler
Claude adds pagination parameters
You miss that three other services expect unpaginated responses
You miss that your mobile app doesn't handle pagination
You ship it and break production
With code intelligence:
You query which services call this endpoint
You see mobile app usage patterns
You find two other endpoints that added pagination last month
You grab those implementations as examples
Claude generates code that matches your existing patterns
You know exactly who needs to review the change
The AI still does the implementation. But it's working with complete information instead of fragments.
The MCP Integration That Changes Everything
The Model Context Protocol (MCP) is how this actually gets wired up in practice.
Instead of copy-pasting code into Claude or Copilot, MCP servers expose codebase context as a set of tools the AI can call. Glue provides an MCP server that lets your AI tools:
Search for function definitions and usages
Find similar code patterns
Query ownership and complexity metrics
Pull up recent changes and their context
Map dependencies between components
This turns your AI from a code generator into something closer to a senior engineer who knows the codebase.
The difference shows up immediately in complex tasks. Migrations, refactorings, adding cross-cutting concerns—anything that touches multiple parts of the system suddenly becomes feasible.
Where AI Tools Still Fall Short
Even with perfect context, AI tools have limits.
They're great at:
Implementing well-defined features
Following existing patterns
Writing boilerplate and tests
Explaining complex code
Suggesting refactoring approaches
They're terrible at:
Understanding business requirements
Making architectural decisions
Knowing what users actually need
Debugging production mysteries
Reading between the lines of ticket descriptions
The best results come from treating AI as a very fast junior engineer. Good at execution, needs clear direction, requires review.
But here's what's interesting: code intelligence platforms help with the human side too. When you can see code health metrics, ownership gaps, and complexity hotspots, you make better decisions about what to build and how to structure it.
You notice that authentication code has six different owners and conflicting patterns. That's not an AI problem—that's an architecture problem. But seeing it clearly lets you fix it before asking AI to implement anything new.
What Engineering Teams Should Actually Use
So what's the practical stack for AI-assisted development in 2024?
For individual coding:
Cursor or Copilot for inline suggestions
Claude for complex implementations
An MCP server that provides codebase context
For team intelligence:
Code indexing that maps your entire system
Automated documentation generation
Complexity and churn tracking
Ownership mapping and gap analysis
The key is integration. Your AI tools need to talk to your code intelligence platform. Otherwise, you're back to manually gathering context.
Glue handles this by indexing your codebase continuously, discovering features through AI analysis, and exposing everything through MCP. Your editor's AI sees the same knowledge graph that powers your documentation and team insights.
The Real Test: Can AI Handle Your Hairiest Bug?
Here's how you know if your setup actually works.
Pick your most complex, cross-cutting issue. The kind that makes senior engineers groan. Maybe it's:
Authentication that spans six microservices
A data migration that touches every model
Error handling that needs consistency across the system
A performance optimization that requires profiling multiple layers
Try to fix it with AI assistance.
If you spend more time gathering context than implementing the fix, your tooling is wrong. If the AI keeps suggesting changes that break related code, you need better code intelligence.
If it works—if you can navigate the codebase through queries, understand impact before making changes, and generate code that fits your patterns—you've got something valuable.
Why This Matters Now
AI coding tools are getting better fast. Claude 3.5 Sonnet writes cleaner code than Claude 3 Opus. GPT-4 Turbo handles larger contexts than GPT-4.
But the core problem remains: AI tools are only as good as the context they receive.
As codebases grow and systems get more complex, the context problem gets worse. You can't solve it by memorizing more of your codebase. You can't solve it by adding more tokens to context windows.
You solve it by building better code intelligence and connecting it to your AI tools.
That's the actual unlock. Not prompt engineering. Not newer models. Better knowledge graphs.
The teams that figure this out first will ship features 3x faster than everyone else. Not because their AI is better—because their AI knows the codebase.