Designing a Self-Maintaining Infrastructure Platform. From Backups to Behavioral Intelligence
For the past few months, I’ve been quietly building something I always wanted in my infrastructure: a system that doesn’t just run backups, monitor metrics, and collect logs, but actually understands them. A system that adapts, responds, and even questions itself when things go sideways.
This isn’t another all-in-one SaaS product or vendor-locked platform. It’s a modular, AI-assisted, self-evolving infrastructure control center, built entirely from open systems, hand-crafted integrations, and a healthy dose of stubborn pragmatism.
The Core Philosophy: Automate What Matters and understand the Rest
The project started as a better way to manage and report on backups. But like most good tools, it started to grow sideways:
- I needed better monitoring → so I added a modular check-agent system.
- I needed logs to mean something → so I connected everything via syslog and Graylog.
- I wanted reporting without micromanaging → so I built a template-based dynamic report engine.
- I needed data from other systems (like Zabbix) → so I synced that too.
- I wanted to know why something failed, not just what → so I started adding smart validation rules and early AI hooks.
This thing isn’t just tracking metrics anymore. It’s reasoning about them.
What It Does (Without Getting Too Nerdy)
Here’s a high-level view of what the platform manages:
- Tiered Backups (XFS + ZFS) with live scheduling, snapshot validation, and predictive planning.
- Monitoring Agents pulling from central rulesets, dynamically tunable and group-assigned.
- Syslog-based ingestion pipeline, pushing agent logs and system events into Graylog, with a custom UDP listener feeding an InfluxDB backend.
- Visual Dashboards in Grafana, linked to alerts, patterns, and anomalies.
- Modular Web Backend for full control over servers, checks, backup settings, network mappings, thresholds, etc.
- Report Generator that pulls from live SQL queries, conditionally includes data, and supports output formats like email, HTML, and JSON.
- Early-stage AI logic, using historical patterns and validation rules to detect misconfigurations and surface root causes before humans even log in.
All of this is stitched together in a way that’s fast, transparent, and controlled entirely by me, not a third-party provider.
AI Is the Icing, Not the Cake
What’s exciting isn’t that it “uses AI”. It’s that AI is simply a layer on top of real-time, curated, high-fidelity data. The platform already knows:
- What ran when
- What failed
- What should have run
- What resources were involved
- What was expected and whether it was reasonable
That makes it incredibly powerful for ad-hoc investigation, trend spotting, or automatic behavior tuning.
Why Build It Yourself?
Because nothing else gave me this level of precision, adaptability, and ownership. I’m a solo developer/sysadmin who doesn’t want to be on call 24/7 but also doesn’t want black boxes managing production systems.
This platform lets me:
- Roll out a new monitoring rule across 100+ servers with a single click.
- Identify which backups haven’t run and why in seconds.
- Detect if a server is underperforming based on context, not just thresholds.
- Customize retention, triggers, checks, and logic per VPS, per group, per rule.
And it’s all driven by clean, structured data, not magic or marketing slides.
What’s Next?
I’m still refining parts, syncing more deeply with Zabbix, improving anomaly detection and basically… I need to rewrite a few parts that are based on too many ‘Hmm, what if….’ moments in deep and dark nights filled with Trash Metal on Spotify.
But the goal is clear:
To create an infrastructure system that doesn’t just react but thinks ahead.
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