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src/content/posts/2026-02-22-parallel-agents-74-processes-one-server.md
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---
title: "Parallel Agents: 74 Processes, One Server"
description: "How I run 74 independent services on a single server with zero downtime -- and what that architecture means for reliability."
date: 2026-02-22
tags: ["infrastructure", "automation", "scaling"]
---
Most people hear "74 processes on one server" and assume it is a stress test. It is not. It is a production system that handles real traffic every day. Here is how I built it and why it works.
## Why 74 Processes?
Each agent in my system is an independent service with its own responsibilities. One handles email. One monitors inventory. One tracks prices. One manages schedules. And so on, 74 times.
I could have combined them into a single application. But isolated processes mean isolated failures. If the email agent crashes, the price tracker keeps working. If one service runs out of memory, nothing else is affected. Each process restarts on its own.
The tradeoff is resource management. And that is where good tooling earns its keep.
## The Architecture
Every service is defined in a single configuration file. A process manager reads that file and keeps all 74 services running, each with its own memory ceiling, restart rules, and log files.
The key settings that make this work:
- **Memory ceilings** -- Each process gets a hard limit. When it exceeds that limit, it restarts automatically. Simple agents get 100MB. Services with caches get 150MB.
- **Restart limits with delays** -- If something crashes 10 times, the system stops restarting it instead of burning CPU in an infinite loop.
- **Consolidated logging** -- Each service writes to its own log file, not a shared dump. With 74 services, organization is the difference between debugging in 30 seconds and 30 minutes.
## The Numbers
Here is the math that makes this viable:
| Metric | Value |
|--------|-------|
| Total processes | 74 |
| Average memory per process | ~75 MB |
| Total memory used | ~5.5 GB |
| Server RAM | 16 GB |
| Headroom for OS and database | ~10 GB |
| Log output per day (unmanaged) | 2 GB |
| Log output per day (with rotation) | ~200 MB |
Comfortable margins, but not wasteful.
## The Watchdog
I built a dedicated monitoring service that checks on every other process once per minute. It looks for three things:
1. **Crashed services** -- anything that has stopped running gets restarted automatically
2. **Crash loops** -- if something has restarted more than five times recently, it flags the issue instead of endlessly restarting
3. **Memory hogs** -- any process consuming more than its share gets reported
This watchdog has caught dozens of silent failures that would have gone unnoticed in a monolithic setup.
## Log Management
74 processes generating logs will fill a disk faster than you would expect. I set up automatic rotation: each log file caps at 10MB, the system keeps 7 days of history, and old logs get compressed. Without this, I was burning through 2GB of raw logs per day.
## Not All Failures Are Equal
The restart strategy varies by importance:
- **Critical infrastructure** restarts immediately with exponential backoff -- if the hub goes down, everything needs it back fast
- **Standard services** restart after a short delay, up to 10 attempts
- **Experimental services** get only 3 restart attempts before staying down for manual review
This tiered approach means I am not applying the same urgency to a background analytics job as I am to the core routing service.
## Five Lessons From Running This in Production
**Isolation beats efficiency.** Yes, 74 separate services use more memory than one big application. But when service number 47 leaks memory at 3 AM, it restarts on its own and the other 73 never notice.
**Name everything clearly.** When you are scanning a list of 74 services, good names are the difference between a 30-second fix and a 30-minute investigation.
**Set up log rotation on day one.** Not after your disk fills up at 2 AM on a Saturday.
**Memory limits prevent outages.** Every process should have a ceiling. Slow leaks become non-events instead of emergencies.
**A watchdog is not optional at scale.** Something that watches the watchers sounds redundant until it catches a silent failure that would have cost you hours.
## What This Means For Your Business
If you are running background services, automation, or any kind of multi-agent system, the lesson is simple: isolation and good process management let you scale on modest hardware. You do not need a Kubernetes cluster or a fleet of servers to run dozens of independent services reliably. One well-configured server with proper monitoring, memory limits, and restart policies can handle more than you think.
The key is discipline in configuration, not throwing more resources at the problem.
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