Course Outline

Introduction to LLMs and Agent Frameworks

  • Overview of large language models in infrastructure automation
  • Key concepts in multi-agent workflows
  • AutoGen, CrewAI, and LangChain: use cases in DevOps

Setting Up LLM Agents for DevOps Tasks

  • Installing AutoGen and configuring agent profiles
  • Using OpenAI API and other LLM providers
  • Setting up workspaces and CI/CD-compatible environments

Automating Test and Code Quality Workflows

  • Prompting LLMs to generate unit and integration tests
  • Using agents to enforce linting, commit rules, and code review guidelines
  • Automated pull request summarization and tagging

LLM Agents for Alert Handling and Change Detection

  • Designing responder agents for pipeline failure alerts
  • Analyzing logs and traces using language models
  • Proactive detection of high-risk changes or misconfigurations

Multi-Agent Coordination in DevOps

  • Role-based agent orchestration (planner, executor, reviewer)
  • Agent messaging loops and memory management
  • Human-in-the-loop design for critical systems

Security, Governance, and Observability

  • Handling data exposure and LLM safety in infrastructure
  • Auditing agent actions and restricting scope
  • Tracking pipeline behavior and model feedback

Real-World Use Cases and Custom Scenarios

  • Designing agent workflows for incident response
  • Integrating agents with GitHub Actions, Slack, or Jira
  • Best practices for scaling LLM integration in DevOps

Summary and Next Steps

Requirements

  • Experience with DevOps tooling and pipeline automation
  • Working knowledge of Python and Git-based workflows
  • Understanding of LLMs or exposure to prompt engineering

Audience

  • Innovation engineers and AI-integrated platform leads
  • LLM developers working in DevOps or automation
  • DevOps professionals exploring intelligent agent frameworks
 14 Hours

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