Research refactoring opportunities in Code Suggestion architecture

As we prepare to move into the next phase of the Code Suggestion feature development, this is a research task to re-evaluate our current architecture and identify any changes we should make proactively.

This issue is not asking for immediate code changes, but rather for investigation and proposals. The goal is to step back and ensure we have an optimal architecture in place across the Monolith, AI Gateway, and Language Server as it relates to performance, maintainability, and logical code grouping for the Code Suggestion feature.

Please go through our current implementation and document any ideas for improvement in any of the following areas:

• Performance optimizations - Are there opportunities to optimize execution time, reduce network calls, cache requests, or improve algorithms?
• Maintainability improvements - Can we improve testability, reduce complexity, improve cohesion, establish more consistency, or enhance readability?
• Modularity - Are there opportunities to better modularize the code to make it more reusable or decoupled?
• Redundancy - Is there any duplicate logic that could be consolidated?
• Dependency management - Are there dependencies between components that could be reduced?
• Error handling - Can the error handling be improved for resiliency?
• Logging/Telemetry - Do we have sufficient logging and telemetry to monitor performance and issues?
• Caching - Could caching help improve performance or reduce external calls?
• Security - Are there any potential security improvements to consider?
• Test coverage - Do we have adequate unit, integration, and e2e test coverage?
• Documentation - Is the architecture and flow properly documented?

The outcome of this investigation should be new issues filed for any tangible proposals identified. Please capture any high-level findings directly in this ticket as well.