Get in Touch

Course Outline

Introduction to Large Language Models and Agent Frameworks

  • Strategic application of large language models in infrastructure automation
  • Fundamental principles governing multi-agent operational workflows
  • Evaluation of AutoGen, CrewAI, and LangChain for DevOps implementation within government contexts

Configuration of LLM Agents for DevOps Operations

  • Deployment of AutoGen and configuration of agent profiles
  • Integration with OpenAI API and other approved large language model service providers
  • Establishment of workspaces and continuous integration/continuous deployment (CI/CD) compatible environments for government use

Automation of Testing and Code Quality Assurance Processes

  • Leveraging large language models to facilitate the generation of unit and integration test cases
  • Utilization of agents to enforce linting standards, commit protocols, and code review guidelines
  • Automated summarization and classification of pull requests

Deployment of LLM Agents for Alert Management and Change Monitoring

  • Development of responder agents designed to address pipeline failure alerts
  • Analysis of system logs and traces using language model capabilities
  • Proactive identification of high-risk changes or infrastructure misconfigurations

Orchestration and Coordination in Multi-Agent DevOps Environments

  • Role-based agent orchestration strategies, including planner, executor, and reviewer functions
  • Management of inter-agent messaging protocols and memory architectures
  • Implementation of human-in-the-loop oversight mechanisms for critical systems

Security Protocols, Governance Frameworks, and Observability Standards

  • Mitigation of data exposure risks and assurance of large language model safety within infrastructure
  • Auditing of agent activities and implementation of scope restrictions for government compliance
  • Monitoring of pipeline performance and analysis of model feedback loops

Operational Use Cases and Custom Scenarios

  • Design of agent-driven workflows for incident response operations
  • Integration of agents with GitHub Actions, Slack, or Jira platforms for government agencies
  • Best practices for scaling large language model integration within DevOps environments

Conclusion and Recommended Next Steps

Requirements

  • Demonstrated proficiency with DevOps infrastructure and continuous integration/continuous deployment (CI/CD) pipeline automation.
  • Familiarity with Python programming and version control systems utilizing Git-based methodologies.
  • Conceptual understanding of Large Language Models (LLMs) or practical experience in prompt engineering techniques.

Target Audience

  • Engineering personnel and platform leads driving innovation through AI-integrated solutions for government applications.
  • Developers specializing in LLMs within automated DevOps environments serving federal missions.
  • DevOps specialists evaluating intelligent agent frameworks to enhance operational capabilities.
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories