From chaos to context: Building an AI dev workflow

This post details the development of agentic AI workflows, persistent memory systems for AI assistants (opencode-memory), and strategies for managing parallel AI sessions. It introduces concepts like Model Context Protocol (MCP) for token efficiency and integrates with broader SDLC knowledge graphs like Orbit. The focus is on building practical, directive-driven AI workflows that amplify engineering productivity, including the use of git worktrees for parallel MR work and the proceduralization of recurring tasks into AI directives.