Enforces engineering governance for AI-driven software projects, ensuring state over prompt, freeze over generate, and audit over output through the 5S workflow.
Enables AI coding assistants to run a machine-verified DESIGN→PLAN→EXECUTE→VERIFY→COMPLETE workflow with human approval gates, state integrity checks, and DAG task scheduling.
Enforces structured, evidence-guided software engineering tasks with cognitive actions (investigate, plan, verify, remember) and persistent state for LLM-based coding agents.
Enforces a rigorous spec-driven engineering workflow for AI coding assistants, requiring requirements, design, and task planning before any code is written.
Enables AI coding agents to enforce spec-driven development and verify code before it is marked done, using six tools that catch invented APIs, scan for hallucinated content, check plugin conformance, sandbox-run tests, validate schemas, and record audit evidence.
Protocol-enforced learning system combining memory-augmented reasoning with workflow automation to improve AI assistant reliability by ensuring they learn from past experiences before making code changes.