LLMs and Agents in DevOps Workflows Training Course

Introduction to LLMs and Agent Frameworks

• The role 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 Tasks

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

Automating Test and Code Quality Processes

• Prompting LLMs to create unit and integration tests.
• Employing agents to enforce linting, commit standards, and code review policies.
• Automating pull request summarization and tagging.

LLM Agents for Alert Management and Change Detection

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

Multi-Agent Coordination in DevOps

• Orchestrating agents with specific roles (planner, executor, reviewer).
• Managing agent messaging loops and memory.
• Incorporating human-in-the-loop designs for critical systems.

Security, Governance, and Observability

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

Real-World Applications and Custom Scenarios

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

Conclusion and Future Steps