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Course Outline
Introduction to Hermes Agent
- What is Hermes Agent and how it differs from IDE-based copilots.
- The concept of the self-improving agent and its closed learning loop.
- Architecture overview: backends, platforms, and tools.
Installation and Setup
- Installing Hermes Agent locally.
- Deploying within Docker containers.
- Remote deployment via SSH, Daytona, Singularity, and Modal.
- Configuring API keys for OpenAI, Anthropic, OpenRouter, and the Nous Portal.
Interacting with the Agent
- Using the CLI interface and basic commands.
- Setting up and utilising a Telegram bot.
- Integrating with Discord and Slack.
- Establishing WhatsApp connectivity.
Built-in Tools
- Web searching and content extraction.
- File operations: reading, writing, editing, and searching.
- Executing terminal commands and bash scripting.
- Image generation and vision analysis.
- Text-to-speech capabilities.
Persistent Memory
- Cross-session memory utilisation with FTS5 recall.
- LLM summarisation for maintaining long-term context.
- Searching and retrieving stored memories.
The Skills System
- Understanding what skills are and how they are created.
- Skill persistence across different sessions.
- Accessing community skills via agentskills.io.
MCP Integration
- Connecting to MCP servers.
- Programmatically extending tool capabilities.
Scheduled Automations
- Using the built-in cron scheduler.
- Setting up recurring tasks and generating reports.
- Distributing automation results across platforms.
Developer Automation Use Cases
- Autonomously running terminal commands.
- Spawning isolated subagents.
- Managing parallel workstreams and batch processing.
Security and Best Practices
- Implementing approval modes for commands and edits.
- Ensuring data privacy on self-hosted infrastructure.
- Utilising environment isolation techniques.
Production Deployment
- Running Hermes Agent on a $5 VPS.
- Adopting serverless deployment patterns.
- Monitoring agent health and reviewing logs.
Troubleshooting
- Addressing common installation issues.
- Debugging tool failures.
- Tuning memory usage and performance.
Summary and Next Steps
- Recap of key capabilities covered in the course.
- Resources provided for continued learning.
- Transitioning to more advanced Hermes topics.
Requirements
- Basic proficiency with command-line terminals and Linux commands.
- Understanding of standard software development workflows.
- General knowledge of AI concepts and large language models.
Audience
- Software developers seeking to integrate AI agents into their workflows.
- DevOps engineers exploring autonomous tooling solutions.
- Technical team leads evaluating AI agent platforms for adoption.
14 Hours
