ASES - AI Scrum Engineering System
Hey everyone, I’ve been working on something called "ASES", and I think it’s finally at a point where it needs real-world testing instead of just me building in a vacuum. This is not just another “AI tool” or prompt setup. It’s basically my attempt at: "Turning LLMs into a structured, end-to-end software development system" --- ## What ASES actually is ASES is a "schema-driven Scrum workflow for AI-assisted development". Instead of: * random prompts * messy context * inconsistent outputs It gives you: * a "full project lifecycle" * "structured artifacts (PRD, HLD, LLD, tasks, tests, etc.)" * and a system where models operate inside that structure --- ## What makes it different ### 1. Everything is structured (schemas + templates) Almost everything in ASES is backed by schemas: * PRD → requirements * HLD / LLD → architecture * Tasks → execution * Decisions → tracked explicitly * Test suites + reports → validation * Sprint summaries + audits → closure So instead of “ask the model and hope for the best” you get: "deterministic, repeatable outputs" --- ### 2. Full Scrum lifecycle (not just tasks) This isn’t just a task runner. It actually maps a full flow: * Planning → PRD / roadmap * Design → HLD / LLD * Execution → sprint-based tasks + snapshots * Testing → structured validation * Closure → audit + summaries Everything lives in a "project structure", not chat history. --- ### 3. Runtime context control (this is the core) Instead of dumping context into every prompt, ASES: * Injects context "just-in-time (per action)" * Uses "layered + scoped context" (global / sprint / execution) * Adjusts how much context to include based on state So the model only sees: > what it actually needs *right now* This is where a lot of the "token efficiency + consistency" comes from. --- ### 4. Model orchestration (multi-model workflows) ASES is designed for using multiple models with "clear roles", not one model doing everything. For example: