Always-On Multi-Agent AI Infrastructure
A persistent agentic-AI platform where multiple specialized agents run continuously, orchestrate across LLM providers, and act on real systems — engineered for uptime, isolation, and governance from the ground up.
The Problem
Most "AI agents" are a script that runs once. The goal here was the opposite: agents that run persistently, hold context across sessions, coordinate with each other, and take real actions — without falling over, and without becoming a security liability.
That raises hard problems most demos never face: keeping always-on agents alive across reboots, isolating their blast radius, orchestrating across multiple model providers, and enforcing what each agent is actually allowed to do.
Architecture
A supervised, multi-agent design. A primary reasoning agent retains context and delegates to specialized sub-agents; every agent runs containerized behind a zero-trust mesh, with a model-agnostic orchestration layer and capability-scoped tool access.
Approach
- Architected a supervisor pattern: one context-holding reasoning agent that delegates to specialized sub-agents, rather than a diffuse swarm — keeping authority centralized and auditable.
- Containerized every agent and put them behind a Tailscale zero-trust mesh with mTLS — eliminating WAN exposure and isolating blast radius per agent.
- Built a model-agnostic orchestration layer so work routes to the right LLM (Claude, Gemini, or local) for cost and capability, with no vendor lock-in.
- Enforced governance in code — capability-scoped tool access and pre-execution guardrails — so an agent can only do what it is explicitly authorized to do.
- Engineered for reliability with process supervision (systemd/launchd) and automated failover so the platform survives reboots and crashes.