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Duration 14 hours
Course Outline
Introduction to LLMs and Agent Frameworks
- The role of large language models in infrastructure automation.
- Core concepts underpinning multi-agent workflows.
- Exploring AutoGen, CrewAI, and LangChain: practical applications in DevOps.
Configuring LLM Agents for DevOps Operations
- Installing AutoGen and setting up agent profiles.
- Utilizing the OpenAI API and integrating other LLM providers.
- Establishing workspaces and environments compatible with CI/CD pipelines.
Streamlining Test and Code Quality Processes
- Using LLM prompts to generate unit and integration tests.
- Employing agents to enforce linting standards, commit rules, and code review guidelines.
- Automating the summarization and tagging of pull requests.
LLM Agents for Alert Management and Change Detection
- Designing responder agents to address pipeline failure alerts.
- Analyzing logs and traces through language model capabilities.
- Proactively identifying high-risk changes or configuration errors.
Coordinating Multi-Agent Systems in DevOps
- Implementing role-based agent orchestration (planner, executor, reviewer).
- Managing agent messaging loops and memory persistence.
- Incorporating human-in-the-loop design for critical system interactions.
Security, Governance, and Observability
- Addressing data exposure risks and LLM safety in infrastructure contexts.
- Auditing agent actions and enforcing scope restrictions.
- Monitoring pipeline behavior and capturing model feedback.
Real-World Applications and Custom Scenarios
- Designing agent workflows for effective incident response.
- Integrating agents with GitHub Actions, Slack, or Jira.
- Best practices for scaling LLM integration within DevOps environments.
Conclusion and Future Directions
Requirements
- Practical experience with DevOps tooling and pipeline automation.
- Proficiency in Python and familiarity with Git-based workflows.
- Foundational understanding of LLMs or prior exposure to prompt engineering.
Audience
- Innovation engineers and platform leads focused on AI integration.
- LLM developers specializing in DevOps or automation domains.
- DevOps professionals exploring the potential of intelligent agent frameworks.
