Managing AI agents on a server requires careful resource planning to avoid system crashes. Based on observations from September 2026, a single agent consumes 150 MB of RAM in standby mode, while an agent processing complex documents can require up to 680 MB. If an agent uses a browser, an additional 400 MB per instance must be allocated.
System stability depends on proper configuration of memory and CPU limits. During mass restarts, concurrent agent initialization creates significant spikes in resource usage. The author recommends launching agents sequentially while monitoring free memory, ensuring at least 1200 MB of free RAM remains between startups to prevent system failure.
Improperly set resource limits can lead to silent performance degradation. In one instance, twelve agents running on a machine with a two-gigabyte limit and one CPU core resulted in a load average of 10.87, which is ten times the norm. The system appeared healthy, but agents responded slowly, highlighting the need to update configuration limits whenever hardware is changed.
Hardware selection involves specific constraints for certain AI services. For instance, agents using Claude must be hosted on servers outside of Russia due to regional restrictions. While some Russian hosting providers offer European locations that accept local payments, the author advises checking historical uptime records rather than relying solely on service level agreements.
