How CodeLogic Works

CodeLogic

Knowledge Graph

Informs AI

  • Profiles and builds a comprehensive knowledge graph

Diagnoses Disorder

  • Differential analysis
  • Impact information
  • Cyclomatic complexity

Extensible

  • CodeLogic Annotations
  • Pipeline integration
  • Continuous capture
Powered By

Directed AI

AI Director

  • Performs in-depth graph analysis
  • Plans and executes workstreams

Coding AI & MCP

  • Foundation of automated coding
  • Supports Interactive scenarios (e.g. IDE)

Automated Process

  • Ties to build system & tooling
  • Event driven loop
  • Drives Directed AI as an automated workload
AI Informed by

Comprehensive Graph Data

Artifact decomposition

  • Bytecode, IL, Source (based on language requirements)
  • Provides baseline dependency / relationship data
  • Extensible agents

Runtime inspection

  • Observes runtime behavior to gather dependencies data that manifests at execution
  • fuzzy hashes to reduce profiling requirements

Database / Data model scanning

  • Schema discovery and related common ORM relationships
  • Code and Server-side stored procedures, functions, packages and trigger decomposition and analysis
AI generates

Workstream Actions and Code

1

The AI performs complex analysis of graph differentials

Mass analysis of graph differential data

2

Generates difference set

3

Writes complex “tickets” for work that needs doing

Effectively prompt generation

4

Directs the Coding AI to write the code

Checking and rechecking

CodeLogic's Process

Automated Process

Ties Directed AI elements into a pipeline integrated automation pattern

Driven by dependency change notifications

  • Automated agents: e.g. Dependabot
  • CodeLogic annotation driven notifications
  • Extensible

Goal: reduce or stop Tech Debt accrual

  • Let’s get AI to do most, or all, of this mundane and painful nonsense

Supported Languages

Seamless Integration

Databases