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 Duration 7 hours

Course Outline

Foundations of OpenClaw and the Safety Framework

  • Defining OpenClaw’s capabilities, limitations, and suitability for government applications
  • Core architectural components: agents, tools, skills, memory, connectors, and approval mechanisms
  • Institutional considerations: data sensitivity, environment isolation, and secure default configurations

Deployment, Configuration, and Initial Agent Execution

  • Verification of prerequisites: Node.js, Git, API credentials, and directory structures
  • Installation of OpenClaw, validation of the setup, and review of the project architecture
  • Integration with an LLM provider, configuration of core parameters, and connectivity verification
  • Execution of a starter agent with read-only permissions, followed by the addition of controlled write operations

Leveraging Built-in Tools and Effective Prompt Engineering

  • Management of standard utilities: file handling, shell commands, and basic web interactions
  • Establishing prompt patterns for deterministic outcomes: constraints, step-by-step planning, and confirmation protocols
  • Auditing agent outputs, tool invocations, and execution traces to identify issues proactively

Application of Skills and Memory Management

  • Implementation and configuration of skills for standardized, repeatable workflows
  • Memory protocols: determining appropriate data retention, exclusion criteria, and safe reset procedures
  • Practical application: developing a workflow with strict memory usage limits and defined termination conditions

Development and Validation of Custom Skills

  • Skill architecture: input/output specifications and OpenClaw’s discovery and execution logic
  • Creation of a business-specific skill (e.g., synthesizing report directories into executive briefs)
  • Testing protocols: verification of sample inputs, expected outputs, error management, and documentation

Integrations, Operational Standards, and Future Directions

  • Integration patterns: chat and ticketing workflows within secure sandbox environments
  • Designing reproducible automation flows: triggers, actions, review stages, approval gates, and handoff procedures
  • Operational requirements: logging, auditability, configuration management, and pilot readiness assessments

Requirements

  • Proficiency with basic command-line operations (directories, paths, environment variables)
  • Capability to install and execute developer tools on a workstation (Git, Node.js)
  • Familiarity with basic JavaScript or scripting (reading code and making minor modifications)

Audience

  • Developers and automation engineers tasked with building AI-powered assistants and internal tools
  • IT and operations professionals seeking to automate recurring support and administrative tasks
  • Technical product owners and team leads evaluating self-hosted AI agent solutions

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