Been lurking here for a while and wanted to share something I’ve been experimenting with, mostly because I want honest feedback on whether this approach is smart or just chaotic. I’m building a relatively complex multi-tenant Telegram bot SaaS. Think: multiple isolated business clients, each with their own customers, delivery drivers, broadcast system, encrypted database, and admin panel. All in one codebase. Python 3.11, SQLite WAL, python-telegram-bot v20+. The interesting part isn’t the project — it’s how I’m building it. The setup: ∙ Claude (claude.ai) as the “architect brain” — I describe problems, Claude thinks them through, writes job files and delegation plans, never touches code directly ∙ GitHub Copilot with Opus 4.6 and Sonnet 4.6 in Agent Mode for complex multi-file refactors ∙ 8 simultaneous Minimax terminals running parallel jobs (M2.7 for critical reasoning tasks, M2.5 for everything else since it’s free) ∙ Claude Code for GSD-style terminal work The workflow: Claude analyzes the codebase state, identifies root causes, then writes 8-9 detailed job files — each one assigned to a specific model with a specific reason why that model fits that task. Each job gets a single file or module to avoid conflicts. All agents compile with py_compile after every change. What actually works well: ∙ Parallel execution cuts what would be a 3-hour session into 45 minutes ∙ Splitting jobs by file prevents agents from stepping on each other ∙ Forcing agents to show their output before calling something “done” catches a lot of hallucinated fixes ∙ Having one model as pure “thinker/planner” and others as “executors” creates a surprisingly clean separation What’s still frustrating: ∙ Agents confidently report “fixed” when the bug is still there ∙ Context loss between sessions means the architect brain needs a detailed “second brain” document to stay oriented ∙ Two agents occasionally implement the same DB function differently and the merge creates subtle bugs My questions for you:
- Has anyone built a proper file-locking system between parallel AI agents? Right now it’s just job assignment by file, but real-time locking would be cleaner. 2. Is there a smarter way to verify that a fix actually works beyond “run py_compile and check output”? 3. Anyone else using a dedicated “planner” model separate from “executor” models? Does the separation actually help or is it just overhead? 4. What’s your experience with Minimax M2.7 vs Sonnet 4.6 for complex Python refactors? Not trying to flex — genuinely curious if others have found better orchestration patterns. This feels like it’s 70% there but the remaining 30% is where most of the time goes. submitted by /u/Brickbybrick030
Originally posted by u/Brickbybrick030 on r/ArtificialInteligence