Future Work and Enhancements

While the Code Analysis Agent demonstrates strong functionality in its current form, several important extensions and improvements have been identified for future development:

Efficiency and Scalability

Processing speed, especially during graph construction and query response generation, remains a bottleneck. Future work should explore optimization strategies, such as:

  • Implementing more aggressive parallelization during code parsing and graph uploading.
  • Utilizing more efficient data structures for representing intermediate graph information.
  • Investigating incremental updates to the Neo4j database rather than full re-uploads.

These changes could significantly reduce initialization times and improve system responsiveness for larger repositories.

Smarter Entity Matching

At present, the system requires strict, fully-qualified names (e.g., src.app.graph) to correctly identify modules and functions. A more robust approach would involve:

  • Implementing flexible matching techniques that map user queries phrased in natural language (e.g., “the graph module”) to underlying code elements.
  • Leveraging LLM-based entity resolution to handle ambiguous references, especially when names are shared across different parts of the codebase.

This enhancement would make the agent feel more conversational and forgiving, particularly in exploratory settings.

Improved Query Precision

The current Cypher-based graph queries sometimes yield noisy or overly broad results, connecting modules through weak or incidental relationships (such as shared use of common libraries like numpy). Future improvements could focus on:

  • Defining stronger notions of “semantic relevance” between nodes.
  • Incorporating path scoring heuristics to prioritize meaningful architectural links over incidental ones.
  • Introducing adjustable query parameters (e.g., “strict mode”) to allow users to control the breadth of relationships retrieved.

This would enable more targeted, insightful exploration of code structures.

Enhanced Agent Collaboration

Currently, the agent router selects either the micro or macro agent exclusively for a given query. Future work could improve the system by:

  • Allowing micro- and macro-agents to communicate or pass intermediate results.
  • Enabling workflows where high-level summaries from the macro agent inform detailed Cypher queries from the micro agent (or vice versa).
  • Building hierarchical reasoning chains that integrate structural overviews with fine-grained relational data.

Such enhancements would allow richer, multi-layered responses, providing users with both strategic overviews and precise technical details in a seamless experience.