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What Is an AI Agent?

An AI agent is a software program that uses a large language model (LLM) as its reasoning engine and has the ability to act on its environment — browsing the web, reading and writing files, sending emails, calling APIs, and executing code. Unlike a standard chatbot that only responds to prompts, an agent can perform multi-step tasks autonomously.

Key characteristics that define an AI agent:

• LLM-powered reasoning — connects to models like GPT-4, Claude, DeepSeek, or open-source alternatives to interpret tasks and plan actions
• Tool access — can browse the web, read files, run code, send emails, connect to external APIs and databases
• Autonomous execution — given a goal, the agent determines the steps, executes them, and reports results without step-by-step human guidance
• Persistent state — has memory, skills, and configuration that persist across conversations and tasks

How AI Agents Connect to LLMs

An AI agent does not run an LLM locally. Instead, it connects to an LLM provider through an API. This is a critical architectural distinction that affects hosting requirements:

• API-based access — The agent sends structured prompts to an LLM provider (OpenAI, Anthropic, DeepSeek, etc.) and receives generated responses. The agent handles the orchestration — deciding what to ask the LLM, executing tool calls, and chaining multiple LLM invocations together.
• Provider flexibility — Multiple LLMs can be configured and swapped without changing the agent's code. The same agent can use GPT-4 for reasoning tasks, Claude for long-context analysis, and DeepSeek for cost-sensitive operations.
• No local GPU required — Because the LLM runs on the provider's infrastructure, the agent's server does not need expensive GPU hardware. This is why agent hosting is fundamentally different from model hosting.

AI Agent Hosting Requirements

Hosting an AI agent requires more than a simple web server. An agent is a full Linux system with runtime dependencies, tool integrations, and persistent state:

Runtime Environment

• Python or Node.js runtime for the agent framework (e.g., Hermes Agent, LangChain, AutoGPT)
• Package managers (pip, npm, uv) for installing skills and dependencies
• System packages for tool integrations (image processing, document parsing, database connectors)

Tool Integrations

• Web browser / search API for research tasks
• Email (SMTP/IMAP) for sending and receiving messages
• File storage integrations (Nextcloud, S3, local filesystem)
• Database and ERP connectors (Odoo, PostgreSQL, etc.)
• API keys for LLM providers and third-party services

State and Persistence

• Conversation memory and task history
• Installed skills, plugins, and custom instructions
• Cron jobs, scheduled workflows, and automation rules
• Model caches, package caches, and downloaded dependencies

Popular AI Agent Frameworks

The following agent frameworks are actively maintained and production-ready as of 2026:

Hermes Agent (by Nous Research)

An open-source agent framework with a built-in skill system, tool library, and cron scheduler. Hermes runs as a non-root user with dedicated tool-access permissions. It supports Telegram, Discord, and web UI as conversation interfaces. The skill system allows adding domain-specific capabilities (cold email, Odoo integration, competitor profiling) without modifying core code. Official Documentation →

Other Frameworks

LangChain, AutoGPT, CrewAI, and OpenAI Assistants API offer alternative approaches to agent design. Most share the same architectural pattern: LLM API access, tool execution, and persistent state management.

What to Look for in an AI Agent Hosting Provider

Based on real operational experience with production agent deployments, here are the criteria that matter:

1. Full Linux instance, not container snapshots — Agents need a real operating system with package managers, cron, SSH access, and persistent storage. Lightweight containers that reset on restart are incompatible with agent workloads.
2. Full-disk backups — As shown in the backup analysis, agents scatter state across the entire filesystem. A proper backup captures everything, not just the home directory.
3. SSH access — The ability to inspect logs, install packages, debug issues, and configure low-level system settings is essential for maintaining production agents.
4. Managed updates — The agent framework, runtime, and dependencies should be maintained by the hosting provider. The user manages the agent's prompt and skills, not its operating system.
5. API key management — LLM provider keys, integration credentials, and tool API tokens must be stored securely and configurable through a dashboard.

How derez.ai Implements These Standards

derez.ai is a managed AI agent hosting platform built on these principles:

• Each agent runs on its own dedicated Linux instance with full root SSH access
• Automatic full-disk daily backups with 7-day retention and one-click restore
• Pre-installed Hermes Agent framework with access to a growing library of skills
• Dashboard for managing agents, passwords, API keys, integrations, and backups
• Coupon codes reduce the first month to as low as $0.00 for evaluation

Further Reading

• Hermes Agent Setup Guide — From zero to a running agent
• Why Backing Up an AI Agent Is So Hard — Real filesystem analysis
• Direct Odoo API vs MCP — Architecture comparison for ERP-connected agents
• Speed Run: Cold Email Agent — Building an email-sending agent
• Speed Run: Odoo Community Agent — Building an ERP-connected agent

derez.ai — Managed AI agent hosting. · Blog · Focus Topics