Hands-On Hermes Agent: Persistent Agents, Memory, Skills, and Real Workflows

Most AI assistants are still used as temporary chat sessions. You ask a question, get an answer, and the context disappears when the session ends.

Hermes Agent is designed around a different model: a persistent agent that can maintain identity, use memory, evolve skills, support multiple profiles, connect to messaging interfaces, and run repeatable workflows.

In this workshop, we will go step by step through how Hermes Agent works, how to set it up, and how to use it for practical workflows such as coding, research, messaging, and scheduled tasks.

This is not a high-level talk about agents. It is a hands-on technical workshop focused on understanding the architecture and building a useful setup you can continue improving after the session.

What You Will Learn

By the end of the workshop, you will understand how to:

• Set up Hermes Agent locally
• Understand the role of the ~/.hermes/ folder
• Configure the SOUL.md identity layer
• Work with the memory system
• Create and manage self-evolving skills
• Understand the role of the Curator
• Use GEPA-style offline self-evolution safely
• Create multiple profiles for different workflows
• Connect Hermes to messaging tools such as Telegram
• Build coding and research workflows
• Schedule recurring tasks using cron
• Apply best practices for safe and reliable persistent agents

Workshop Curriculum

Module 1 — Persistent Agents: The Mental Model

We start by understanding what Hermes Agent is and why persistent agents matter. We will compare temporary chat-based assistants with persistent agents that can maintain identity, memory, and reusable workflows over time.

Topics covered:

• What Is Hermes Agent?
• Why Persistent Agents Matter
• Core Mental Model
• When to Use Hermes

Module 2 — Hermes Agent Architecture

In this module, we break down the main components of Hermes Agent and how they work together. The focus is on building a clear mental model before moving into setup and workflows.

Topics covered:

• High-Level Architecture
• SOUL.md Identity Layer
• Memory System
• Self-Evolving Skills
• Curator
• GEPA and Offline Self-Evolution

Module 3 — Installation and Local Setup

We then move into the practical setup. You will install Hermes Agent, inspect the local folder structure, and create your first useful agent configuration.

Topics covered:

• Installation
• ~/.hermes/ Folder
• First Useful Agent
• Multiple Profiles

Module 4 — Messaging and External Interfaces

Once the local setup is ready, we will look at how Hermes can interact through messaging interfaces such as Telegram. We will also discuss where guardrails become important when an agent is exposed to external input.

Topics covered:

• Telegram/Messaging
• Messaging-based workflows
• Safety considerations for external interfaces

Module 5 — Practical Workflows

This module focuses on real use cases. We will look at how Hermes can support coding, research, and recurring scheduled tasks.

Topics covered:

• Coding Workflow
• Research Workflow
• Scheduling With Cron
• Turning workflows into repeatable routines

Module 6 — Best Practices, Limitations, and Risks

Persistent agents are powerful, but they also introduce new risks. We will close the workshop by discussing safe usage patterns, review gates, limitations, and where Hermes is useful today.

Topics covered:

• Best Practices
• Limitations and Risks
• Safe self-evolution
• Human review
• Practical next steps

After the workshop, you will have:

• A working Hermes Agent setup
• A configured identity layer using SOUL.md
• A basic memory-aware agent profile
• Example coding and research workflows
• A scheduled cron-based workflow
• Example configuration files and prompts
• Access to the workshop recording
• Step-by-step technical notes you can revisit later

This workshop is designed for:

• Developers interested in persistent agents
• AI engineers exploring memory, skills, and agent workflows
• Researchers and technical writers who want reusable AI workflows
• Builders using tools like Claude Code, Cursor, or other coding agents
• Anyone who wants a practical understanding of agent architecture beyond simple chatbots

Prerequisites

You do not need a deep machine learning background.

You should be comfortable with:

• Basic terminal usage
• Installing developer tools
• Following a technical setup guide
• Using AI assistants or coding tools

Some scripting experience is helpful, but not required.