LLMs and Agents in DevOps Workflows Training Course

Foundations of LLMs and Agent Frameworks

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

Configuring LLM Agents for DevOps

• Installing AutoGen and defining agent profiles
• Integrating OpenAI API and other LLM providers
• Preparing workspaces and CI/CD-compatible environments

Automating Test and Code Quality Processes

• Using prompts to generate unit and integration tests
• Applying agents to enforce linting, commit rules, and code review standards
• Automating pull request summarization and tagging

LLM Agents for Alert Management and Change Detection

• Creating responder agents for pipeline failures
• Analyzing logs and traces with language models
• Identifying high-risk changes or misconfigurations proactively

Multi-Agent Coordination in DevOps

• Role-based agent orchestration (planner, executor, reviewer)
• Managing agent messaging loops and memory
• Designing human-in-the-loop mechanisms for critical systems

Security, Governance, and Observability

• Mitigating data exposure and ensuring LLM safety in infrastructure
• Auditing agent actions and limiting operational scope
• Monitoring pipeline behavior and model feedback

Real-World Applications and Custom Scenarios

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

Wrap-up and Future Directions