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Course Outline

Foundations of Hermes Agent

  • Understanding Hermes Agent’s role and positioning within developer workflows
  • Contrasting local AI agent workflows with cloud-based coding assistants
  • Key features, constraints, and common application scenarios

Preparing the Local Environment

  • Setting up the workstation and installing necessary dependencies
  • Deploying Hermes Agent and verifying the runtime configuration
  • Configuring access to local models and foundational settings
  • Executing an initial workflow to validate the environment setup

Utilizing Core Components

  • Effective use of prompts, instructions, and contextual data
  • Managing memory and persistent state within local workflows
  • Leveraging skills and reusable patterns for routine coding tasks
  • Safely managing tool execution and defined boundaries

Designing Practical Code Assistance Workflows

  • Defining clear workflow objectives, inputs, and expected results
  • Building workflows for code interpretation, review, and debugging
  • Structuring prompts to ensure consistent and valuable agent responses
  • Handling local files and repositories with appropriate security measures

Integration with Developer Tools

  • Interacting with repositories, files, and command-line utilities
  • Facilitating testing and code review processes
  • Designing workflows that seamlessly fit into daily development routines

Safety, Privacy, and Organizational Governance

  • Restricting tool access to minimize risky actions
  • Ensuring sensitive code and data remain within local boundaries
  • Auditing logs, outputs, and workflow execution traces
  • Establishing team policies for secure, agent-assisted development

Practical Lab: Creating a Secure Local Coding Assistant

  • Constructing a basic Hermes Agent workflow for code support
  • Incorporating prompts, memory features, and specific tools
  • Testing the workflow against realistic development tasks
  • Optimizing the workflow for reliability, usability, and security

Troubleshooting and Future Steps

  • Addressing common setup and configuration challenges
  • Diagnosing workflow errors and ambiguous outputs
  • Identifying areas for improvement and planning adoption strategies

Requirements

  • Working knowledge of software development processes and source control systems
  • Experience utilizing command-line interfaces and development environments
  • Fundamental programming proficiency

Target Audience

  • Developers seeking to integrate local AI agents into their coding workflows
  • Technical leads accountable for maintaining secure development pipelines
  • DevOps and platform engineers supporting internal AI infrastructure
 14 Hours

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