Multi Agent
25 articles about multi agent.
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
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.
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
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.
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.
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
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
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.
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.
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.
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
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
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.
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
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