Technical Context
The idea of running 20 Claude Code agents simultaneously on a Mac sounds ridiculous until I actually look at the CPU, memory, and disk usage. It immediately reveals a simple truth: the problem isn't the "agents" themselves, but the lack of a proper queue, parallelism limits, and a coherent AI architecture for local execution.
If I let all agents start at once, the machine doesn't work—it stutters. I get noise, swapping, latency spikes, file system conflicts, network competition, and context window contention. It's especially chaotic if I have an editor, terminals, project indexing, and a few other background services running alongside.
I wouldn't try to treat these symptoms manually. The basic approach is to place a queue manager between tasks and workers, limit concurrency based on job type, and isolate heavy steps. Instead of "20 agents doing everything," it should be more like 3 coding agents, 2 for review, 1 context assembler, and the rest waiting for a slot.
If you're using proxies or local LLMs via Ollama, it's best not to experiment without strict limits. In practice, setting OLLAMA_NUM_PARALLEL=1 and a low OLLAMA_MAX_LOADED helps prevent models from eating up unified memory and crashing the system. Plus, monitoring with `ollama ps` quickly shows which models are actually holding memory versus just creating the illusion of multitasking.
Another point I often stress to my team is that not every agent needs to be equally "smart." I would delegate minor subtasks to lighter models or even deterministic logic, reserving expensive reasoning for specific, high-value steps. This isn't just optimization; it's proper artificial intelligence integration, where resources are allocated based on the task's value.
Impact on Business and Automation
For a business, the takeaway is very down-to-earth: more agents do not equal higher speed. Without queues and priorities, I can easily create a system that looks impressive in a demo but consumes developer time and breaks predictability in real-world use.
The winners are those who build AI automation like a pipeline: ingest, planning, execution, review, and retry. The losers are those who simply multiply agents and hope their hardware can handle the load.
At Nahornyi AI Lab, I solve these issues not with the number of agents but with execution architecture: deciding where a local run is needed, where inference is better offloaded to a separate node, where a queue is necessary, and where an LLM should be removed from a step entirely. If your processes are already hitting this kind of chaos, we can analyze your workflow and design an AI solution development process that accelerates your team instead of just making your Mac overheat.