Get in Touch

Course Outline

Introduction to Large Language Models and Agent Frameworks

  • Application of large language models in infrastructure automation
  • Foundational principles of multi-agent workflows
  • Application of AutoGen, CrewAI, and LangChain for DevOps operations for government

Configuration of LLM Agents for DevOps Functions

  • Installation of AutoGen and configuration of agent profiles
  • Utilization of OpenAI API and alternative LLM service providers
  • Establishment of workspaces compatible with CI/CD pipelines

Automation of Testing and Code Quality Processes

  • Employment of prompts to generate unit and integration tests via LLMs
  • Use of agents to enforce linting standards, commit protocols, and code review guidelines
  • Automation of pull request summarization and classification

LLM Agents for Alert Management and Change Detection

  • Design of responder agents to address pipeline failure notifications
  • Analysis of logs and traces using language models
  • Proactive identification of high-risk changes or configuration errors

Multi-Agent Coordination in DevOps Environments

  • Role-based orchestration of agents (planner, executor, reviewer)
  • Management of agent messaging loops and memory states
  • Implementation of human-in-the-loop controls for critical systems

Security, Governance, and Observability

  • Management of data exposure risks and LLM safety in infrastructure
  • Auditing of agent actions and enforcement of operational scope restrictions
  • Monitoring of pipeline behavior and model feedback loops

Real-World Applications and Custom Scenarios

  • Development of agent workflows for incident response
  • Integration of agents with GitHub Actions, Slack, or Jira for government workflows
  • Best practices for scaling LLM integration within DevOps

Summary and Next Steps

Requirements

  • Proficiency in DevOps instrumentation and automated pipeline management
  • Competency in Python programming and Git-centric operational workflows
  • Familiarity with Large Language Models (LLMs) or practical experience in prompt engineering

Target Audience

  • Innovation engineers and platform leadership focused on AI integration
  • LLM developers engaged in DevOps practices or automation initiatives
  • DevOps specialists investigating intelligent agent frameworks for government applications
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories