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 underlying multi-agent workflows.
• Applying AutoGen, CrewAI, and LangChain to DevOps use cases.

Configuring LLM Agents for DevOps

• Installing AutoGen and defining agent profiles.
• Utilizing OpenAI APIs and alternative LLM providers.
• Establishing workspaces and CI/CD-compatible environments.

Automating Testing and Code Quality

• Using prompts to drive LLM generation of unit and integration tests.
• Enforcing linting standards, commit rules, and code review guidelines via agents.
• Automating pull request summarization and tagging processes.

LLM Agents for Alerts and Change Detection

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

Multi-Agent Coordination in DevOps

• Orchestrating role-based agents (planner, executor, reviewer).
• Managing agent messaging loops and memory structures.
• Implementing human-in-the-loop designs for critical systems.

Security, Governance, and Observability

• Mitigating data exposure risks and ensuring LLM safety in infrastructure.
• Auditing agent actions and defining scope restrictions.
• Monitoring pipeline behavior and collecting model feedback.

Real-World Applications and Custom Scenarios

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

Summary and Next Steps