Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to LLMs and Agent Frameworks
- The role of large language models in infrastructure automation.
- Fundamental concepts underlying multi-agent workflows.
- Practical applications of AutoGen, CrewAI, and LangChain in DevOps.
Configuring LLM Agents for DevOps Operations
- Installation of AutoGen and configuration of agent profiles.
- Integration with the OpenAI API and alternative LLM providers.
- Establishing workspaces and CI/CD-compatible environments.
Automating Test and Code Quality Processes
- Utilizing LLM prompts to generate unit and integration tests.
- Enforcing linting standards, commit rules, and code review guidelines via agents.
- Automating pull request summarization and tagging.
LLM Agents for Alert Management and Change Detection
- Creating responder agents to address pipeline failure alerts.
- Log and trace analysis powered by language models.
- Identifying high-risk changes or misconfigurations proactively.
Coordinating Multi-Agent Systems in DevOps
- Orchestrating role-based agents (planner, executor, reviewer).
- Managing agent messaging loops and memory states.
- 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 limiting their operational scope.
- Monitoring pipeline behavior and capturing model feedback.
Real-World Applications and Custom Scenarios
- Designing agent workflows for incident response strategies.
- Integrating agents with GitHub Actions, Slack, or Jira.
- Best practices for scaling LLM integration within DevOps ecosystems.
Summary and Future Steps
Requirements
- Practical experience with DevOps tooling and pipeline automation.
- Proficiency in Python and Git-based workflows.
- Familiarity with LLMs or prior exposure to prompt engineering concepts.
Target Audience
- Innovation engineers and leads responsible for AI-integrated platforms.
- LLM developers specializing in DevOps or automation contexts.
- DevOps professionals looking to explore intelligent agent frameworks.
