How to Run Multiple AI Agents Without Breaking Your Codebase скачать в хорошем качестве

How to Run Multiple AI Agents Without Breaking Your Codebase

AI coding works like a queue: One prompt, wait, review, next. Breaks with multiple agents in same codebase. Context leaks, branches collide, files overwrite. Coordination chaos. Problem isn't models. It's infrastructure. AI agents need isolation. Solution: Git worktrees. Multiple branches checked out simultaneously. Different directories, same repo, independent. Agent 1: feature-auth worktree. Agent 2: feature-logging worktree. Agent 3: feature-api worktree. No interference. Clean context. Review separately. Merge when ready. Plus: Match models to tasks. Planning: reasoning model. Execution: fast model. Review: different context. At FlytBase: Multiple agents, own worktrees, appropriate models, different branches. Parallel development that works. Hard parts: Context bleeding, silent failures, model limits, setup overhead, branch management, cost. Solution: Clean context per agent, AI checks, model optimization, reusable templates, naming conventions. Real bottleneck: Keeping context clean. Infrastructure solves this, not prompts. Video shows: Actual setup, worktree config, model assignments, multi-agent workspace, real failures + fixes. Scale AI coding: Infrastructure for isolation. Parallel agents with clean context. 🔗 https://chat.whatsapp.com/CSg6OChjPmC... 🌐 https://www.aiatflytbase.com/ #AIForDevelopers #AICoding #MultiAgent #DeveloperProductivity Subscribe.