Implements a three-tier complexity scoring system (LOW/MEDIUM/HIGH) that dynamically adjusts AI reasoning effort and token allocation based on COBOL code patterns. Features adaptive re-chunking on output exhaustion, escalation loops for reasoning recovery, and thrash guards to prevent infinite retry cycles. This component solves the challenge of optimizing AI resource usage for variable-complexity code processing tasks.

Provides semantic boundary detection for code chunking that prioritizes language-specific structural units (DIVISION > SECTION > Paragraph) over arbitrary line or character counts. Preserves semantic integrity during chunking to maintain context for AI processing. This reusable component addresses the fundamental problem of splitting code for AI processing while preserving meaning and context across chunks.

Manages cross-chunk type consistency with SQLite persistence, ensuring consistent variable-to-type mappings across code chunks. Tracks forward references to not-yet-processed code and resolves them during reconciliation passes. This solves the critical challenge of maintaining type safety and consistency in chunked AI-powered code generation systems.

Plans incremental scans based on file changes and semantic versioning rather than simple timestamps, avoiding re-processing of unchanged files while ensuring semantic changes are detected. This component provides sophisticated cache invalidation logic that understands when code changes actually affect analysis results, significantly improving efficiency for large codebases.

External prompt template system using Markdown files with model-specific projections and caching. Provides clean separation of prompt engineering from agent logic, supports easy prompt updates without code changes, and includes performance optimizations through projection caching. This reusable component solves the challenge of managing and evolving AI prompts in production LLM applications.