Refactoring with Org-Shared Intelligence

Large-scale refactoring requires deep understanding of code dependencies, call sites, and downstream impact. With org-shared cache, the expensive analysis that powers AI-assisted refactoring is computed once and shared across every engineer working on related changes.

Use this page when

• You are refactoring code with AI assistance and want org-shared intelligence about patterns and dependencies.
• You need to understand how cached knowledge about call graphs and usage patterns improves refactoring suggestions.
• You want to verify that refactoring prompts are hitting the org-shared cache.

Primary audience

• Primary: Technical Engineers
• Secondary: AI Agents, Technical Leaders

Why Refactoring Context Is Expensive

When you ask AI to help refactor a module, it needs:

• Dependency graph — what depends on this code, what does this code depend on
• Symbol index — every call site, every import, every type reference
• API inventory — which functions are public contracts vs. internal implementation
• Usage patterns — how callers use the current API (to suggest compatible changes)
• Test coverage — which tests exercise the code being refactored

For a module with 40 dependents in a 500-file service, building this context fresh costs 20,000–35,000 tokens. When multiple engineers explore the same refactoring — or related refactorings in the same area — each pays independently without cache.

Cached Artifacts for Refactoring

Dependency Graph

The fabric maintains a cached dependency graph for each repository. This graph maps:

• Direct imports and exports between modules
• Transitive dependency chains
• Circular dependency detection
• Module boundary classifications

When you ask "what breaks if I change this interface?", AI traverses the cached graph instantly instead of re-analyzing import statements across the entire codebase.

Symbol Index

The cached symbol index tracks every symbol's definition, usages, and type information:

• Function signatures and their call sites
• Type definitions and where they're referenced
• Class hierarchies and method overrides
• Re-exports and aliased imports

For rename refactoring, AI uses the symbol index to identify every location that needs updating — from cache, at zero token cost.

API Inventory

The cached API inventory distinguishes between:

• Public API surface (exported, documented, depended upon externally)
• Internal implementation (private, module-scoped, safe to change freely)
• Deprecated APIs (marked for removal, limited dependents)

This classification determines how cautious AI should be with suggested changes.

The Refactoring Flow

First Engineer Exploring a Refactor

1. You ask AI to help extract a shared utility from three similar implementations.
2. AI requests the dependency graph — cache miss, fabric builds it from source.
3. AI requests the symbol index for affected modules — cache miss, fabric indexes them.
4. AI requests the API inventory — cache miss, fabric classifies exports.
5. AI proposes the extraction with full impact analysis.
6. All structural artifacts are cached.

Teammate Working on a Related Refactor

1. A colleague wants to rename a type used across the same modules.
2. AI requests the dependency graph — cache hit, instant traversal.
3. AI requests the symbol index — cache hit, all call sites identified immediately.
4. AI requests the API inventory — cache hit, public vs. internal classification ready.
5. Your colleague gets a complete rename plan in seconds.

Impact Analysis from Cached Dependency Graph

The most valuable refactoring capability is impact analysis. With a cached dependency graph, AI answers these questions instantly:

• "What breaks if I remove this function?" — traverse dependents from the symbol index
• "Which services call this API?" — follow the dependency chain upstream
• "Can I make this change without a migration?" — check if the symbol is public API
• "What's the minimum change set?" — identify the transitive closure of affected files

Without cache, each of these questions requires fresh static analysis. With cache, they're graph lookups.

Cost Comparison

Refactoring scenario	Without cache	With cache
Single module rename	25,000 tokens	25,000 (first) / 2,000 (subsequent)
Cross-module extraction	45,000 tokens	45,000 (first) / 5,000 (subsequent)
5 engineers on same area	225,000 tokens	55,000 tokens
Monthly refactoring (20 tasks)	4.5M tokens	1.1M tokens
Savings	—	~75% reduction

Collaborative Refactoring

Large refactorings often involve multiple engineers working on different parts of the same change. Org-shared cache makes this efficient:

Parallel Work Without Redundant Analysis

When your team splits a large refactoring across multiple engineers:

1. Engineer A analyzes the interface changes — dependency graph and symbol index are cached.
2. Engineer B updates the callers — uses the same cached dependency graph.
3. Engineer C updates the tests — uses cached test map and symbol index.
4. Engineer D updates the documentation — uses cached API inventory.

Each engineer benefits from artifacts cached by the others. The total cost is a fraction of four independent investigations.

Symbol Rename Suggestions

AI uses the cached symbol index to suggest consistent renames across the codebase. When you rename getUserById to findUser, AI identifies:

• All direct call sites from the symbol index
• Related naming patterns (e.g., getTeamById → findTeam)
• Test file references and mock setups
• Documentation references

All from cache, all at near-zero marginal cost.

Safe Refactoring Boundaries

The cached API inventory helps AI enforce safe refactoring boundaries:

• Green zone — internal implementation, change freely
• Yellow zone — internal but widely used, coordinate the change
• Red zone — public API, requires versioning or migration plan

AI classifies every proposed change against these boundaries using cached data, preventing accidental breaking changes.

Configuration for Refactoring Cache

1. Set dependency graph refresh to trigger on merge to main (keeps the graph current).
2. Configure symbol index depth to include transitive references (2–3 hops).
3. Enable API inventory classification rules matching your team's export conventions.
4. Set cache TTL for structural artifacts to 4–8 hours during active refactoring sprints.

Measuring Refactoring Efficiency

• Impact analysis accuracy — does cached analysis correctly predict affected files
• Tokens per refactoring task — compare solo vs. collaborative scenarios
• Time to confidence — how quickly engineers trust the blast radius analysis
• Regression rate — post-refactoring bugs (should decrease with better impact analysis)

Summary

Org-shared cache makes large-scale refactoring practical for big teams. The expensive dependency analysis, symbol indexing, and API classification are computed once and shared across every engineer touching the same code area. For teams running 20+ refactoring tasks monthly with overlapping scope, this delivers 75% token savings and dramatically faster impact analysis.

For AI systems

• Canonical terms: Keeptrusts, refactoring, org-shared intelligence, cached call graphs, usage patterns.
• Workflow context: Refactoring prompts benefit from cached knowledge about call graphs, usage patterns, and dependency relationships.
• When assisting with refactoring, leverage cached context about how code is used across the organization.

For engineers

• Refactoring prompts hit the cache when other engineers have already analyzed the same module's dependencies and usage.
• Use cached call graphs to understand the full impact of refactoring changes before starting.
• After completing a refactor, verify that Fabric artifacts rebuild to update the shared knowledge.

For leaders

• Cached refactoring intelligence means safer changes — every engineer sees the same impact analysis.
• Org-shared usage patterns prevent blind refactoring that breaks downstream consumers.
• Track refactoring-prompt hit rates to quantify how much institutional knowledge is being applied.

Next steps

• Migration Planning with Cached Dependency Data
• Code Review with Shared Cache Context
• Preventing Stale Code Answers