Running coding agents on 2+ machines taught me the hard part isn't the agents, it's the control plane
Summary
Running coding agents across multiple machines reveals that managing the control plane is more challenging than the agents themselves, offering insights into distributed agent orchestration.
Similar Articles
The hard part of agents is not building one. It is operating five.
The article discusses the operational challenges of running multiple AI agents in production, emphasizing observability, recovery, and session management over the initial development of a single agent.
Lessons from months of running a mixed fleet of coding agents on the same repos
The article shares lessons learned from using multiple AI coding agents on the same code repositories over several months, covering insights on their effectiveness and challenges.
Nobody talks about the actual bottleneck with no-code agents: it's not building one, it's running 50 of them together
Discusses that the real challenge with no-code AI agents is not building a single agent, but managing and running many of them together efficiently.
running multiple coding agents in parallel broke in ways i didn't expect. the three that actually bit me
The article discusses three unexpected problems when running multiple coding agents in parallel: conflicts over shared working tree, runtime collisions (database, ports), and difficulty detecting stuck agents. Solutions include using per-agent git worktrees, isolated runtimes, and monitoring remaining gap metrics.
"At what point does adding another agent actually hurt your system? Asking because my 6-agent pipeline is slower and less reliable than my old 2-agent one
A developer shares real-world experiences with AI orchestration frameworks (LangGraph, CrewAI, AutoGen), noting trade-offs between ease of prototyping and production reliability, and asks the community about handling failures, human-in-the-loop, and token costs.