Multi Agent Guides

2 guides

Explore Multi Agent guides and tutorials on vybecoding.ai. Each guide is a structured, multi-lesson course covering practical techniques, real-world examples, and hands-on exercises for developers at every skill level. Whether you are just getting started with Multi Agent or looking to deepen your expertise, these guides provide the depth and clarity to move forward with confidence.

2 guides availableAverage read time: 16 minutes10 total viewsMost popular level: intermediate
How to Run 10+ Parallel Claude Code Agents Without Merge Conflicts - programming guide cover
advanced
ai and-machine-learning

How to Run 10+ Parallel Claude Code Agents Without Merge Conflicts

A first-hand playbook for orchestrating a fleet of parallel Claude Code agents on one repository — the git worktree merge protocol, per-agent browser isolation, and the shared-state files that must never be touched by two agents at once. Built from running this exact setup in production.

#claude-code#multi-agent#git-worktrees+2 more
4
0
Build a Self-Improving Data Pipeline with Multi-Agent Quality Gates - programming guide cover
intermediate
ai development

Build a Self-Improving Data Pipeline with Multi-Agent Quality Gates

A practical Anthropic SDK walkthrough of Meta's Autodata closed-loop pattern — four subagents (Generator, Evaluator, Filter, Optimizer), exact acceptance thresholds, and a TypeScript implementation that turns inference compute into higher-quality training data.

#anthropic-sdk#synthetic-data#multi-agent+4 more
6
0

Multi Agent guides on vybecoding.ai

How to Run 10+ Parallel Claude Code Agents Without Merge Conflicts

A first-hand playbook for orchestrating a fleet of parallel Claude Code agents on one repository — the git worktree merge protocol, per-agent browser isolation, and the shared-state files that must never be touched by two agents at once. Built from running this exact setup in production.

Level: advanced · 8 min read

Build a Self-Improving Data Pipeline with Multi-Agent Quality Gates

A practical Anthropic SDK walkthrough of Meta's Autodata closed-loop pattern — four subagents (Generator, Evaluator, Filter, Optimizer), exact acceptance thresholds, and a TypeScript implementation that turns inference compute into higher-quality training data.

Level: intermediate · 24 min read