LLMs and Agents in DevOps Workflows Training Course

Introduction to LLMs and Agent Frameworks

• Role of large language models in infrastructure automation
• Core principles of multi-agent workflows
• Applications of AutoGen, CrewAI, and LangChain in DevOps

Configuring LLM Agents for DevOps Tasks

• Installation of AutoGen and agent profile configuration
• Utilization of OpenAI API and alternative LLM providers
• Establishing workspaces and CI/CD-ready environments

Automating Test and Code Quality Processes

• Prompting LLMs for unit and integration test generation
• Enforcing linting, commit standards, and code review protocols via agents
• Automated pull request summarization and labeling

LLM Agents for Alert Management and Change Detection

• Creating responder agents for pipeline failure notifications
• Log and trace analysis using language models
• Proactive identification of high-risk changes or misconfigurations

Multi-Agent Coordination in DevOps

• Role-based orchestration (planner, executor, reviewer)
• Management of agent messaging loops and memory
• Human-in-the-loop strategies for critical systems

Security, Governance, and Observability

• Managing data exposure and LLM safety in infrastructure
• Auditing agent behavior and limiting scope
• Monitoring pipeline behavior and model feedback

Real-World Scenarios and Custom Applications

• Architecting agent workflows for incident response
• Integrating agents with GitHub Actions, Slack, or Jira
• Strategies for scaling LLM integration within DevOps

Conclusion and Future Directions