LLMs and Agents in DevOps Workflows Training Course

Introduction to LLMs and Agent Frameworks

• The role of large language models in infrastructure automation
• Foundational concepts in multi-agent workflows
• Application of AutoGen, CrewAI, and LangChain in DevOps scenarios

Configuring LLM Agents for DevOps Operations

• Installing AutoGen and defining agent profiles
• Utilizing the OpenAI API and alternative LLM providers
• Establishing workspaces and CI/CD-ready environments

Streamlining Test and Code Quality Processes

• Using prompts to drive LLM generation of unit and integration tests
• Applying agents to enforce linting, commit standards, and code review protocols
• Automating the summarization and tagging of pull requests

Leveraging LLM Agents for Alerts and Change Detection

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

Coordinating Multi-Agent Systems in DevOps

• Orchestrating role-based agents (planner, executor, reviewer)
• Managing agent messaging loops and memory states
• Implementing human-in-the-loop strategies 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 collecting model feedback

Practical Use Cases and Custom Scenarios

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

Summary and Recommended Next Steps