How to Run Multiple AI Agents Without Breaking Your Codebase

Описание: 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.

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🌐 https://www.aiatflytbase.com/

#AIForDevelopers #AICoding #MultiAgent #DeveloperProductivity

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