AI for Software Development FAQ: The Shift-Everywhere Approach
Shift-left is over. That entire philosophy — catching bugs earlier, testing sooner, security scanning at the start — made sense when humans were the bottleneck. When the primary constraint was "developers making mistakes late in the cycle."
But AI doesn't work that way.
AI needs context everywhere, all the time. Your LLM doesn't care if you "shift left." It cares whether it can see your authentication middleware when generating an API endpoint. Whether it knows that processPayment() was just refactored when writing integration tests. Whether it understands that Sarah owns the checkout flow and Tom owns payment processing when suggesting architectural changes.
This is shift-everywhere. AI that operates at every stage because the entire codebase is indexed, understood, and accessible.
Shift-left emerged from waterfall hangovers. The idea: catch problems before they get expensive. Test during development instead of QA. Security scan before deployment. Front-load the quality work.
This made sense. Fixing a bug in production costs 100x more than catching it in your IDE. So we built pre-commit hooks, CI pipelines, and static analysis gates. We caught errors earlier.
But here's what happens with AI coding assistants:
You ask GitHub Copilot to implement OAuth. It generates code that conflicts with your existing authentication layer because it doesn't know you already have a session management system. You discover this three days later during code review.
You ask Claude to refactor a service. It suggests patterns that violate your team's architectural decisions from two sprints ago. Those decisions live in a Notion doc nobody linked to the codebase.
You use Cursor to write tests. It generates mocks for an API that changed yesterday. The tests pass. They're testing the wrong contract.
The problem isn't the AI's capabilities. The problem is information distribution.
Shift-left assumed the constraint was human error at specific stages. Shift-everywhere recognizes the constraint is contextual awareness across all stages simultaneously.
What Shift-Everywhere Actually Means
Shift-everywhere means your AI tools have real-time access to:
Code structure and dependencies. Not just "this file imports that file." But "this authentication middleware is used by 47 endpoints, last modified by three different teams, and has complexity hotspots in session validation."
Feature boundaries. Which code implements which feature? When you ask AI to "modify the shopping cart," it should know that's 12 files across 4 directories, not just cart.js.
Ownership and expertise. Who wrote this? Who maintains it? Who should review changes? AI shouldn't suggest refactoring the payments module without knowing that's Tom's domain and he's on vacation.
Recent changes and patterns. What's churning? What got refactored? What patterns did the team just adopt? If everyone moved to async/await last month, AI shouldn't generate new Promise chains.
Documentation that's actually current. Not what you wrote six months ago. What the code does today.
This is where platforms like Glue become relevant. You can't maintain this context manually. You can't expect developers to document everything perfectly. You need automated code intelligence that indexes the codebase continuously, discovers features, and keeps AI systems synchronized with reality.
Real Examples of Shift-Everywhere in Practice
Example 1: Feature Implementation
Traditional approach: PM writes requirements. Dev reads docs, writes code, hopes it integrates properly. PR review catches integration issues.
Shift-everywhere approach: Before writing any code, AI knows:
This feature touches authentication (owned by security team)
Similar features use this specific middleware pattern
The payments service just changed its API contract
Three files have high complexity and churn — avoid adding logic there
You query your code intelligence system: "Show me where user preferences are stored and how they're accessed." It returns actual code locations, ownership info, and recent changes. Now when you ask your AI assistant to "add a notification preference," it generates code that integrates correctly the first time.
Example 2: Bug Investigation
Production bug. Users report checkout failures. Traditional flow: check logs, grep codebase, ping teammates on Slack, spend two hours finding the relevant code.
Shift-everywhere: Ask "What code executes during checkout and has changed in the last week?" Code intelligence maps the entire checkout flow, shows recent modifications, highlights that validatePayment() was refactored yesterday. AI assistant can now help debug because it sees the full context.
This isn't science fiction. This is what happens when your codebase is continuously indexed and AI has access to that index.
Example 3: Code Review
Traditional review: Reviewer manually checks if new code follows patterns, integrates properly, meets standards. Misses subtle issues. Approves. Bugs emerge.
Shift-everywhere review: AI pre-checks the PR against:
Team's actual coding patterns (learned from recent commits)
Architectural decisions (discovered from code structure)
Complexity and churn metrics (is this adding technical debt?)
Human reviewer sees AI's analysis: "This changes a high-churn file owned by another team. Consider extracting this logic." The human makes the judgment call. But they're not doing mechanical pattern-matching anymore.
The Infrastructure Requirements
You can't implement shift-everywhere with manual processes. You need:
Continuous indexing. Your codebase changes constantly. Yesterday's index is useless. The intelligence layer needs to update with every commit.
Semantic understanding. Not just syntax trees. Actual feature boundaries. What code implements what functionality? This requires AI to analyze the codebase and discover implicit relationships.
Integration with AI tools. Your index needs to be accessible to Cursor, Copilot, Claude, whatever your team uses. This is where standards like MCP (Model Context Protocol) matter — they let AI tools query your code intelligence without custom integrations for each tool.
Team context. Code doesn't exist in isolation. Ownership, expertise, team structure — this context makes AI suggestions practical instead of theoretical.
Glue handles this by continuously analyzing your codebase, discovering features, generating documentation from actual code, and exposing everything through MCP. Your AI assistants query Glue for context. They get current information. They generate better code.
What This Looks Like in Practice
Your standup: "I need to add rate limiting to the API."
Traditional workflow: Search codebase for "rate limit". Find three different implementations. Wonder which one to use. Ask in Slack. Wait for response. Implement. Hope it's consistent.
Shift-everywhere workflow: Ask code intelligence: "How is rate limiting currently implemented?" Get back: middleware pattern used in 23 endpoints, owned by platform team, last updated two weeks ago, here's the code. Ask AI assistant: "Add rate limiting to the new webhook endpoint using our standard pattern." It generates code that matches your existing implementation.
Time saved: maybe an hour. But multiply that across your team, across a year. And consider the quality improvement — the new code actually integrates correctly because AI saw the context.
Or: You're refactoring. Traditional approach means manually tracing dependencies, updating callers, hoping you found everything.
Shift-everywhere: Query "What depends on this function?" Get the complete dependency graph. Ask AI: "Refactor this function and update all callers." It can do this safely because it sees the full picture.
The Skeptic's Questions
"Isn't this just fancy code search?"
No. Code search finds text. Code intelligence understands semantics. When you search for "payment", you get 400 results. When you query code intelligence for "show me the payment processing flow", you get the 12 files that actually implement payments, with ownership and dependency information.
"Won't this make developers lazy?"
Wrong question. Developers shouldn't waste time on mechanical context-gathering. They should think about architecture and tradeoffs. If AI can handle "update all callers of this function," developers can focus on "should we even have this function?"
"What about security? Isn't this exposing the codebase?"
Your AI assistant already has access to your code. That's how it generates suggestions. Code intelligence just organizes that access better. Though yes, you need to think about access controls and what context gets exposed to which systems. This matters.
"How is this different from IDE features?"
Your IDE understands syntax and local scope. It doesn't understand features, ownership, or team patterns. It can't tell you "this code is high-churn and owned by a team that's underwater." It can't discover that three services implement authentication differently and maybe that's a problem.
The Implementation Path
You don't flip a switch and get shift-everywhere. You build toward it:
Start with indexing. Get your codebase continuously indexed. This is table stakes. You need current information.
Add feature discovery. Let AI analyze your code and discover what actually implements what. This creates the semantic layer.
Integrate with AI tools. Make the index accessible to your Copilot/Cursor/Claude setup. MCP makes this cleaner than custom integrations.
Layer in team context. Ownership, expertise, team structure. This makes AI suggestions practical for your organization.
Iterate on the feedback loop. AI generates code, you review, you capture patterns from what gets approved. Feed this back into the intelligence layer.
Glue is built for this progression. You connect your repo, it starts indexing and discovering features. You integrate MCP with your AI tools. They start making better suggestions because they have better context. You spend less time on mechanical tasks and more time on actual engineering.
Where This Goes
The endgame isn't "AI writes all code." It's "AI has the same context as senior developers."
When a senior dev joins your team, they spend weeks learning the codebase. Where's the authentication code? How do we handle errors? What are our patterns? They build a mental model.
Shift-everywhere means AI builds that mental model too. Continuously. Automatically. And shares it across every AI tool your team uses.
This changes what's possible. Not because AI gets smarter. Because AI gets context.
And that's the actual revolution. Not better models. Better information architecture.