Your deployment field guide.
From choosing an architecture to operating and recovering an AI agent. Start where you are, and work through the decisions that matter.
Where should your AI agent run?
Choose between an API-backed agent server and self-hosted inference by examining workload, data boundaries, hardware, and operational responsibility.
The real cost of running an AI agent
Build a useful operating budget beyond the server price: model requests, retries, storage, backups, egress, and the resources that keep billing while idle.
Plan your first Vultr agent server
A reference workflow for choosing a server, preparing SSH access, separating public and private ports, and verifying a deployment before it handles real work.
Design an agent deployment with Docker Compose
A practical reference architecture for an API-backed agent, with explicit service boundaries, persistent state, private ports, secrets, and a recoverable update process.
Put an agent behind a domain and HTTPS
Plan DNS, Caddy certificate automation, private upstream connections, authentication, and ongoing verification without mistaking a padlock for access control.
An operations checklist for an agent with real tools
Define action boundaries, protect secrets, preserve useful logs, and prepare a recoverable deployment before granting an agent access to real systems.
Back up an agent you can actually restore
Identify durable state, choose an application-consistent backup method, separate failure domains, and require real restoration evidence before trusting a recovery plan.
Move an agent server without losing control
Plan a reversible cutover, avoid two active writers, reconcile in-flight actions, and retire old infrastructure with verified data and billing cleanup.
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