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Duration 14 hours
Course Outline
Foundations of LLMs and Agent Frameworks
- The role of large language models in infrastructure automation
- Core principles of multi-agent workflows
- Applying AutoGen, CrewAI, and LangChain in DevOps contexts
Configuring LLM Agents for DevOps
- Installation of AutoGen and setup of agent profiles
- Integration with OpenAI API and other LLM service providers
- Preparation of workspaces and CI/CD-aligned environments
Enhancing Test and Code Quality via Automation
- Prompting LLMs to generate unit and integration tests
- Utilizing agents to enforce linting, commit standards, and code review protocols
- Automated generation of pull request summaries and labels
Applying LLM Agents to Alert Management and Change Tracking
- Creating responder agents for pipeline failure notifications
- Analyzing logs and traces with language models
- Proactive identification of high-risk changes or configuration errors
Coordinating Multi-Agent Systems in DevOps
- Orchestrating agents by role (planner, executor, reviewer)
- Managing agent communication loops and memory retention
- Incorporating human-in-the-loop strategies for critical systems
Security, Governance, and System Observability
- Mitigating data exposure risks and ensuring LLM safety in infrastructure
- Auditing agent behavior and limiting operational scope
- Monitoring pipeline performance and collecting model feedback
Practical Applications and Custom Scenarios
- Architecting agent workflows for incident response
- Connecting agents with GitHub Actions, Slack, or Jira
- Best practices for scaling LLM integration within DevOps
Conclusion and Future Directions
Requirements
- Familiarity with DevOps tooling and pipeline automation
- Proficiency in Python and Git-based workflows
- Basic understanding of LLMs or experience with prompt engineering
Target Audience
- Innovation engineers and leads in AI-integrated platforms
- LLM developers specializing in DevOps or automation
- DevOps professionals exploring intelligent agent frameworks
