Tutorial
This section guides you from zero to a production-ready agent in progressive steps. Each step builds on the previous one.
Framework Note
This tutorial uses LangGraph, the recommended framework for building stateful agents. AgentVisor™ also supports interactive exec mode for running shells and AI coding tools. See Supported Frameworks for details.
Learning Path
| Step | Title | What You'll Learn |
|---|---|---|
| 1 | Your First Agent | Create a chatbot, run it, understand the code |
| 2 | Configuring Your Agent | Environment variables with .env files |
| 3 | Debugging Your Agent | Logging, log levels, access logs |
| 4 | Configuring AgentVisor | Runtime config file, environment variables, precedence |
| 5 | Adding Policies | Policy-based access control with MPE |
| 6 | Securing Your Agent's API | TLS encryption, identity, OIDC (OpenID Connect) |
| 7 | Connecting External Tools | MCP servers, tool discovery, tool policies |
| 8 | Deploying Your Agent | Build images, deploy to production |
Prerequisites
Before starting, ensure you have:
- Docker installed and running
- AgentVisor CLI installed (see Getting Started)
jqinstalled (used throughout to pretty-print API responses in curl examples)
Next Steps
Start with Your First Agent to create and run an AI chatbot, then continue through the learning path to understand configuration, debugging, policies, and production deployment.