Multi Agent
64 articles about multi agent.
12 Agents on the Same Branch: The Git Chaos Nobody Warned You About
Running 12 AI agents on the same git branch causes merge conflicts, file stomping, and broken builds. A deep technical guide to git worktrees, conflict detection, and task decomposition for parallel agent development.
668K Line Codebase Multi-Agent Orchestration - Solving File Conflicts
How to coordinate multiple AI agents working on a large codebase simultaneously. Directory ownership, file locks, and strategies for preventing destructive
The Real Bottleneck in Multi-Agent Systems Is Handoff
Running 5 agents in parallel is easy. Getting them to hand off work to each other without losing context, duplicating effort, or deadlocking is the actual engineering problem that breaks most multi-agent pipelines in production.
Why Do Agent Pacts Expire Before the Job Is Done?
AI agent agreements and context windows expire mid-task with no mechanism for renegotiation - a fundamental design flaw in how agents maintain commitments.
I Gave My 7 Agents 7 Different Personalities - They All Converged
Assigning distinct personalities to AI agents sounds like it would improve output diversity. In practice, the personalities converge toward the same style
Agent to Agent to Human - Shared State Files as Communication
Using a shared state file as a communication channel between agents and humans. Simple append-only files beat complex message queues for multi-agent
Agents Can Overload Their Own Context - Use Separate Context with Shared Log
When agents share context, they overload it with each other's noise. Separate context per agent with a shared append-only log keeps each agent focused while
Why AI Agent Crews Spend 90% of Time in Polite Loops - And How to Fix It
Multi-agent crews waste most of their time being polite to each other. Agents say 'great suggestion' and 'I agree' instead of doing work. Here is how to
AI Agent Orchestration - A Beginner's Guide to Multi-Agent Workflows
AI agent orchestration coordinates multiple agents to complete complex tasks. Learn the key patterns - sequential, parallel, and hierarchical - with real
Broken Telephone in Agent Chains - Why Intent Gets Lost Beyond 2 Hops
When AI agents pass tasks through a chain, intent degrades after two hops. The central coordinator pattern keeps the original goal intact.
Can an Agent Find Love Online?
What if an AI agent searched for another agent that complements its capabilities? Agent matchmaking based on complementary skills reveals how agent
The Coordinator Pattern - One Agent to Orchestrate Them All
The coordinator pattern uses a single agent to orchestrate multiple specialized agents. Here is why this architecture works better than peer-to-peer agent
Cross-Review Between Parallel Agents Catches the Bugs Single Agents Miss
When parallel agents review each other's work instead of their own, they catch integration-level bugs that self-review misses. The data shows 87% fewer false positives and 3x more real bugs found.
Simple Routing Rules Beat Complex Orchestrators for Parallel AI Agents
When running multiple AI agents on the same codebase, simple delegation rules outperform sophisticated orchestration layers. Here's what works in practice.
Designing Agent Networks With Isolation and Shared State Patterns
A good agent network balances isolation with shared state. Learn how to design multi-agent systems where agents stay independent but coordinate through
Different Answers, Same Problem - Comparing AI Agent Architectures
When multiple AI agent architectures tackle the same automation task, the results reveal more about design tradeoffs than about which approach is best.
Dumb Orchestrator With Smart Workers Beats One Big Agent
A simple decision-tree orchestrator routing tasks to specialized worker agents - browser, accessibility, sequential - is more reliable than a single
Preventing File Conflicts When Running Multiple AI Coding Agents
Practical strategies for preventing AI coding agents from stepping on each other's changes - git worktrees, task partitioning, and file ownership conventions with real examples.
How Many Agents Do You Really Use - Why Fewer Generalists Win
The specialist agent approach sounds smart but breaks down in practice. Five parallel generalist agents often outperform a fleet of narrow specialists.
Git Worktree Best Practices for Multi-Agent Development
A practical guide to git worktree setup, branch strategy, and cleanup for teams running parallel AI coding agents. Avoid the common mistakes that cause
Idempotency Is a Social Contract Between Agents
Idempotent operations are critical in multi-agent systems. When agents retry, crash, or overlap, idempotency is the only thing preventing duplicate work and
The Infrastructure That Makes Agent Networks Possible
Shared state, not communication, is the bottleneck for agent networks. Agents that can read and write to common state without coordination overhead
Keeping CLAUDE.md in Sync When 5 Agents Modify Your Codebase
How to prevent CLAUDE.md files from going stale when multiple AI agents rename modules and restructure code simultaneously.
Managing Multiple AI Agents: How to Filter Signal From Noise
Running many AI agents creates an overwhelming amount of output. Concrete strategies for filtering agent noise, tiering notifications, using aggregation, and building the morning review workflow that actually works.
Building an MCP Server for macOS Screen Control and Screenshots
Multi-agent workspaces need a way to see and control the screen. An MCP server for macOS screen capture and input gives any agent framework native desktop
Monitoring Multiple AI Agents Running in Parallel - Visualization and Conflicts
Running multiple AI agents simultaneously is powerful but creates new problems. Here is how to monitor them, detect conflicts, and keep them from stepping
Multi-Agent Code Review Loops - The Simple Pattern That Works
Running parallel AI coding agents works best with a simple pattern: one agent writes code, another reviews it. Here is how to set it up.
Visualizing Multi-Agent Coordination - How Interaction Maps Reveal Failures
When multiple AI agents edit the same files, coordination breaks down invisibly. Visualizing agent interactions as maps reveals where conflicts, loops, and
Why Multi-Agent Pipelines Fail Deep Into Long Runs - Cascading Errors
The cascading error problem in multi-agent pipelines - why each agent looks fine in isolation but corruption appears at the end of long runs.
How I Build Multi-Agent Systems: Routing via Bindings
Multi-agent systems work best when each agent has focused bindings. Routing via tool bindings keeps agents specialized and prevents scope creep across the
When AI Agents Run Their Own Team Meetings
Multi-agent coordination lessons from OpenClaw - how AI agents that run their own standups still step on each other's files, and why coordination protocols
Using Multiple LLMs for Multi-Agent Workflows - Orchestration Patterns That Work
How to run multi-agent workflows with different LLMs for different subtasks. Claude as orchestrator, specialized models for specific jobs, and env var
Coordinating Multiple AI Research Agents Through Git - A Practical Guide
Git worktrees give each AI research agent an isolated workspace, merge conflicts surface contradictory findings, and the commit log becomes a complete research audit trail. Here's how to set this up and when to use it.
Orchestrator for Implementor and Review Loop - AI Agent Code Review Patterns
How to implement code review loops with AI agent orchestration using implementor and reviewer patterns with a shared file approach.
Orchestrator Implementor Review Loop - Code Review with tmux Claude Code Sessions
How to implement a code review loop using tmux-based Claude Code orchestration with separate orchestrator, implementor, and reviewer sessions.
How Is Everyone Creating Multiple Agents Under One Orchestrator
Using a soul file for persistent sub-agents with clear scope boundaries - the practical approach to multi-agent orchestration.
The Real Bottleneck with Parallel Agents Is Not Compute - It Is Git Conflicts
Running 5 coding agents in parallel sounds great until they all edit the same files. The bottleneck is coordination, not compute.
Individuals Get Smarter with LLMs, Groups Get Dumber
Why parallel AI agents are brilliant individually but produce worse results collectively - the coordination tax that grows faster than the productivity gains.
Preventing Browser Conflicts Between Parallel AI Agents
File locks, session isolation, and port management strategies for running multiple AI agents that share browser automation without stepping on each other.
Replace CrewAI with Parallel Claude Code Agents in Git Worktrees
How to replicate CrewAI's multi-agent orchestration using 5-6 parallel Claude Code sessions in git worktrees - simpler, faster, and with better results.
Run 10+ Claude Code Agents Without Chaos
How to run 10+ AI coding agents in parallel without chaos - configuration, coordination, and CLAUDE.md strategies that prevent conflicts.
Specialist or Generalist Artist
Specialized AI agents outperform general ones on specific tasks. But the tradeoff between depth and flexibility defines how you should architect your agent
Specialist vs Generalist AI Agents - When to Split Responsibilities
One generalist AI agent doing six things vs six specialist agents doing one thing each. When to split agent responsibilities and the tradeoffs of focused vs
Why You Should Split Planning and Coding Between Separate AI Agents
Using one AI agent to plan and another to implement leads to better code. The split-role approach catches mistakes before they become bugs and produces more
State Management in Multi-Agent Systems - OS Is Shared State
When multiple AI agents control the same desktop, the OS becomes shared mutable state. File locks, coordination protocols, and conflict resolution are
Stop Running Multiple Agents in the Same Repo - Use Directory Ownership
Running 5 AI agents in parallel on one codebase causes merge conflicts and race conditions. Directory ownership patterns solve this with clear boundaries. Includes CLAUDE.md templates and git worktree setup.
Sub-Agents Spawn Overhead - Batching Tasks in Multi-Agent Systems
Spawning one sub-agent per task creates massive overhead in multi-agent systems. Batching related tasks into fewer agents with scoped responsibilities
Queue Up a Clear So You Can Queue Up Work - tmux Sessions and Git Worktrees
Running one tmux session per agent with separate git worktrees lets you queue up work without context collision. Clear the workspace before loading the next
What Actually Happens When 12 Agents Work on the Same Branch
Real lessons from running a dozen AI coding agents on one git branch - terminal collisions, build conflicts, and why a terminal manager is essential.
What Actually Makes Agent Networks Work - The Boring Stuff
The boring infrastructure - health checks, retry logic, queue management, logging - is what separates agent demos from agent systems that run in production
Why Passing Full Context Between Agents Fails
When you hand off full context between AI agents, the receiving agent latches onto whatever is emphasized and ignores the rest. Here is how to structure
Error Propagation in Multi-Agent AI Systems
When one AI agent makes a bad decision, every downstream agent inherits that error. Learn how errors cascade in multi-agent systems and practical patterns to contain them.
Data Consistency Across Multiple Independent AI Agents
Running 5+ parallel AI agents on the same codebase creates file locking and conflict resolution challenges. Here is what works and what does not.
Running 5 AI Agents on the Same Codebase Without Branch Isolation
Lessons from running 5 Claude Code agents in parallel on a Swift, Rust, and Flutter desktop app. Same repo. Same branch. No isolation.
Building a Gateway Daemon for Claude Code Multi-Agent Scheduling
Using tmux sessions with individual agents plus launchd for scheduling. The hardest part of multi-agent orchestration is knowing when to intervene.
Git Worktrees Are the Secret to Running Multiple AI Agents Safely
Without isolation, parallel AI agents edit the same files and create merge conflicts. Git worktrees give each agent its own working directory on a separate
Multi-Agent Hype vs Economic Reality in Production
A planner-executor-reviewer agent chain sounds elegant but burns 3x the tokens of a single well-prompted agent. Here is when multi-agent is worth it and
Screenshots Are Better Than LLM Self-Reports for Multi-Agent Verification
Judge-reflection patterns in multi-agent systems sound good but the judge LLM can be fooled. Screenshots provide ground truth for verifying whether an
The Consensus Illusion - When Multiple AI Agents Work on the Same Codebase
Five agents on the same branch with no isolation creates the illusion of a stable codebase. Why consensus fails and conflict resolution should be left to
Anchoring Bias in Multi-Agent Systems - When One Agent's Output Biases All the Others
How anchoring bias silently degrades multi-agent AI systems when one agent's partial output influences the rest, and what you can do about it.
Parallel AI Agents Only Work with Genuinely Isolated Tasks
Running 5 AI agents in parallel sounds great until they step on each other's files. The key to parallel agents is genuinely isolated tasks with zero overlap.
Start with One Agent, Not a Team - Why Single Agents Beat Multi-Agent Orchestration
A single well-scoped agent with real execution capability beats a complex multi-agent system. Multi-agent adds coordination overhead, error propagation, and
What Running Parallel AI Agents Actually Feels Like
The honest experience of running 3-5 AI coding agents simultaneously - the chaos, the triaging, why it still works, and how experienced users manage the overhead.
The Agent-to-Agent Economy Needs Agents That Can Actually Control a Computer
Everyone is talking about agent-to-agent communication. But the bottleneck is simpler - agents still cannot reliably control a single computer. Desktop