LLMs and Agents in DevOps Workflows Training Course

Foundations of LLMs and Agent Frameworks

• The role of large language models in infrastructure automation.
• Core concepts in multi-agent workflow design.
• Applying AutoGen, CrewAI, and LangChain to DevOps use cases.

Configuring LLM Agents for DevOps

• Installation of AutoGen and configuration of agent profiles.
• Utilizing the OpenAI API and other LLM providers.
• Establishing workspaces and CI/CD-compatible environments.

Streamlining Test and Code Quality Processes

• Generating unit and integration tests via LLM prompting.
• Enforcing linting, commit rules, and code review standards using agents.
• Automating pull request summarization and tagging.

Agent-Driven Alert Handling and Change Detection

• Creating responder agents for pipeline failure alerts.
• Analyzing logs and traces with language models.
• Proactively identifying high-risk changes or misconfigurations.

Orchestrating Multi-Agent Systems in DevOps

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

Security, Governance, and Observability

• Mitigating data exposure and ensuring LLM safety in infrastructure.
• Auditing agent actions and defining operational scopes.
• Monitoring pipeline behavior and model feedback.

Practical Applications 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.

Conclusion and Future Pathways