The Ultimate Guide to Enterprise AI Context Management
Your AI coding assistant is expensive and kind of stupid.
Not because the model is bad. GPT-4, Claude, Gemini — they're all remarkable. The problem is they're working blind. You ask them to refactor a payment flow, and they hallucinate API endpoints that don't exist. You want them to identify technical debt, and they flag code that was deliberately written that way for a reason your team documented three months ago.
The issue isn't intelligence. It's context.
Why AI Fails in Enterprise Codebases
Here's what happens when you ask an AI to help with real work in a 500K+ line codebase:
You: "Update the authentication middleware to support OAuth2"
Except your auth middleware is actually split across @auth/core, @auth/providers, and middleware/auth-gateway.ts. It integrates with three different services. Two teams own different parts of it. There's a migration in progress that changed everything two weeks ago.
The AI doesn't know any of this. It can't. Its context window is a tiny peephole into your codebase.
Traditional RAG (Retrieval Augmented Generation) helps, but not enough. RAG grabs relevant code snippets based on semantic similarity. That works for documentation chatbots. It fails for codebases because:
Code relationships aren't semantic — UserService doesn't import anything with "user" in the name. It imports @db/postgres, @auth/jwt, and @events/publisher. Semantic search misses these completely.
Context is hierarchical — Understanding one function requires understanding its file, its module, its feature, and its team's conventions. RAG gives you fragments.
Time matters — Code written last week is different from code written last year. Recent churn indicates instability. RAG treats all code equally.
Enterprise AI context management isn't about throwing more tokens at the problem. It's about building a knowledge graph of your codebase that AI can actually navigate.
What Context Actually Means
Context in a codebase has layers:
Structural context: This file imports these modules. This function calls these endpoints. This API route hits this database table. This is knowable and deterministic.
Semantic context: This feature handles checkout. This code is payment-related. This endpoint is part of the admin dashboard. This requires AI to infer, but it's learnable.
Temporal context: This file changes every week (unstable). This code hasn't been touched in two years (stable or abandoned). This team merged 40 PRs here last month (active ownership). This is measurable from git history.
Human context: Sarah owns this. The mobile team depends on it. There's a Slack thread about deprecating it. This is the hardest to capture but the most valuable.
Most AI coding tools only understand structural context, and barely. They know what's imported in the current file. Maybe they can follow one level of dependencies if you're lucky.
That's not enough.
Building a Context Graph That Actually Works
A proper context graph needs to index six types of entities:
1. Code Structure
Files, functions, classes, interfaces, types. The easy stuff. But you need more than just "this function exists" — you need:
Call graphs (what calls what)
Dependency graphs (what imports what)
Type relationships (what extends/implements what)
API route mappings (which URLs hit which handlers)
Most codebases have 10-50K of these entities. You can't dump them all into a context window. You need to query them intelligently.
2. Features and Capabilities
Here's where it gets interesting. Your codebase doesn't organize itself by features. It organizes itself by files and folders. But when someone asks "how does password reset work?", the answer spans multiple files, routes, database tables, and email templates.
Feature discovery requires AI agents that traverse your code and say "these 23 files collectively implement password reset." This is what Glue does automatically when it indexes your codebase — it discovers features by analyzing code relationships, not folder structure.
3. Database Schema
Your ORM models are code. Your migrations are code. But the actual current database schema is often nowhere in your codebase in a queryable format.
AI needs to know: this API writes to the users table. That table has these columns. This column has a foreign key to accounts. That relationship is important when someone asks to add a new user field.
4. Ownership and Expertise
Who wrote this code? Who maintains it? Who's committed to it recently? This metadata is in git, but it needs to be indexed and queryable.
When AI suggests a change to @payments/stripe-integration, you need to know that Sarah owns it, she's on vacation, and the last three people who modified it all left the company. Maybe suggest a different approach.
5. Health Metrics
Code health isn't subjective. It's measurable:
Churn rate: How often does this file change?
Complexity: Cyclomatic complexity, file size, function length
Test coverage: Does this code have tests?
Recency: When was this last modified?
Coupling: How many files depend on this?
High churn + high coupling + low coverage = technical debt hotspot. AI should know this before suggesting changes.
6. Documentation and Decisions
READMEs, ADRs (Architecture Decision Records), inline comments, Jira tickets, Slack discussions. Most of this lives outside your codebase but provides critical context.
The best context graphs pull this in and connect it to code. This PR added rate limiting because of that security incident. This function is weird because of that customer requirement.
How Enterprise Teams Actually Use This
Let's get concrete. Here's how a context graph changes AI coding:
Scenario 1: Refactoring High-Risk Code
Without context: AI suggests refactoring PaymentProcessor.process() to be async. Looks good. You merge it. The mobile app breaks because it was calling this synchronously in a critical path you didn't know about.
With context: Your context graph knows:
This function is called by 3 API routes and 2 background jobs
One of those routes (/api/checkout) has 99.9% uptime requirements
The mobile team's CheckoutManager depends on synchronous behavior (there's a comment in their code about it)
This file has been modified 47 times in the last 3 months (high churn, unstable)
AI now says: "This refactor is risky. The /api/checkout endpoint is critical and the mobile app expects synchronous behavior. Consider creating a new async version alongside the existing one and migrating callers gradually."
That's actually useful advice.
Scenario 2: Adding New Features
Without context: You ask AI to add two-factor authentication. It generates code that looks reasonable but doesn't integrate with your existing auth system, doesn't follow your team's patterns, and reimplements logic that already exists in @auth/providers.
With context: Tools like Glue map your existing authentication feature across all related code. AI sees:
Your auth system already uses @auth/core with JWT tokens
There's a VerificationCodeService that handles email codes (reusable for 2FA)
The frontend has a <OTPInput> component (already built, just not wired up)
Your auth middleware has a plugin architecture specifically designed for this
Three competitors already have 2FA (gap analysis shows this is urgent)
AI generates an implementation that extends your existing patterns instead of creating new ones. It suggests using the existing components. It points out that you're already 80% of the way there.
Scenario 3: Understanding Technical Debt
Without context: You ask "where's our worst technical debt?" AI looks at individual files and says "this file is long and has high complexity."
With context: Your context graph combines churn, complexity, ownership, and test coverage to surface:
legacy-billing-sync.ts has been modified 89 times this year
It has zero tests
It's 3000 lines long
Original author left 18 months ago
Current ownership is unclear (5 different people have touched it recently)
It's imported by 23 other files (high coupling)
It integrates with an external API that's being deprecated next quarter
That's not just "tech debt." That's a ticking time bomb. Your context graph bubbles it to the top with actual business justification for fixing it.
The MCP Integration Changes Everything
Here's where this gets really interesting. The Model Context Protocol (MCP) is an open standard that lets AI assistants query external context sources.
Instead of dumping your entire codebase into a context window, MCP lets AI ask questions about your code:
"Show me all API routes that write to the users table"
"Who owns the authentication middleware?"
"Which files have the highest churn in the payments feature?"
"What features do our competitors have that we don't?"
This is how Glue works with Cursor, Copilot, and Claude. Your AI coding assistant gets real-time access to your context graph. It can navigate your codebase intelligently, not just pattern-match on code snippets.
The difference is night and day. Your AI stops hallucinating endpoints and starts suggesting changes that actually make sense in your architecture.
What You Should Actually Do
If you're serious about AI-assisted development at scale:
Stop treating your codebase like a pile of text files. It's a graph. Index it as one.
Capture more than code. Git history, ownership, health metrics, and feature boundaries are all context. They all matter.
Make context queryable. AI needs to ask "what depends on this?" and get accurate answers in milliseconds, not grep your repo and guess.
Connect AI tools properly. If your AI assistant doesn't have MCP access to your context graph, it's working with one hand tied behind its back.
Measure AI effectiveness. How often do suggestions actually get merged? How often do they introduce bugs? Context quality is measurable.
The teams that figure this out will ship faster with AI. The teams that don't will keep paying for expensive AI assistants that provide surprisingly little value.
Your codebase is too complex for AI to understand from a context window. Build the infrastructure to feed it real context, or accept that it's going to keep being expensive and kind of stupid.