LLMs and Agents in DevOps Workflows Training Course

Introduction to LLMs and Agent Frameworks

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

Setting Up LLM Agents for DevOps Tasks

• Installing AutoGen and configuring agent profiles.
• Leveraging OpenAI API and other LLM providers.
• Establishing 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.
• Automating 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.
• Proactively detecting 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

• Managing data exposure and LLM safety within infrastructure.
• Auditing agent actions and restricting operational 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