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Duration 7 hours
Course Outline
OpenClaw Foundations and Safety Model
- Understanding OpenClaw: its definition, limitations, and ideal use cases
- Key concepts: agents, tools, skills, memory, connectors, and approval mechanisms
- Corporate considerations: data sensitivity, environment isolation, and secure default settings
Setup, Configuration, and Initial Agent Execution
- Verifying prerequisites: Node.js, Git, API keys, and workspace directories
- Installing OpenClaw, confirming the setup, and reviewing the project structure
- Linking an LLM provider, configuring core settings, and testing connectivity
- Launching a basic agent with read-only permissions, gradually introducing controlled write capabilities
Utilizing Built-in Tools and Effective Prompting
- Managing common utilities: file systems, shell commands, and basic web interactions
- Prompting strategies for consistent results: defining constraints, planning steps, and requiring confirmations
- Analyzing agent responses, tool invocations, and execution traces to identify potential issues early
Implementing Skills and Memory
- Adding and configuring skills to streamline repetitive workflows
- Memory management: determining what to store, what to exclude, and how to safely reset data
- Practical task: creating a small workflow that utilizes memory responsibly, including a defined termination condition
Developing and Testing a Custom Skill
- Understanding skill architecture, input/output handling, and OpenClaw's discovery process
- Creating a business-focused skill (e.g., summarizing a directory of reports into a concise brief)
- Testing methodology: defining sample inputs, expected outcomes, error management, and documentation
Integrations, Operations, and Future Steps
- Integration strategies: handling chat and ticket workflows within a secure sandbox
- Designing consistent automation processes: triggers, actions, reviews, approvals, and handoffs
- Operational essentials: logging, auditability, configuration control, and a pilot readiness checklist
Requirements
- Familiarity with basic command-line operations (folders, paths, environment variables)
- Ability to install and execute developer tools on a workstation (Git, Node.js)
- Foundational experience with JavaScript or scripting (reading code and making minor adjustments)
Target Audience
- Developers and automation engineers seeking to build AI-driven assistants and internal tools
- IT and operations professionals looking to automate routine support and administrative tasks
- Technical product owners and team leads assessing self-hosted AI agent solutions
