Coordination
11 articles about coordination.
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.
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
The Shared Memory Problem with Autonomous AI Agents
Running autonomous AI agents overnight sounds great until they repeat themselves because they have no shared memory. Why agent coordination requires
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
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
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.
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.
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.