LLMs and Agents in DevOps Workflows Training Course
Large Language Models (LLMs) and autonomous agent frameworks such as AutoGen and CrewAI are transforming the way DevOps teams automate critical tasks like change tracking, test generation, and alert triage. These technologies enable human-like collaboration and decision-making processes.
This instructor-led live training, available both online and onsite, is designed for advanced engineers seeking to architect and implement DevOps automation workflows driven by Large Language Models (LLMs) and multi-agent systems.
Upon completion of this training, participants will be capable of:
- Integrating LLM-based agents into CI/CD pipelines for intelligent automation.
- Automating test generation, commit analysis, and change summaries using agent technologies.
- Coordinating multiple agents to triage alerts, generate responses, and deliver DevOps recommendations.
- Constructing secure and maintainable agent-driven workflows using open-source frameworks.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live lab environment.
Customization Options
- To arrange customized training for this course, please contact us.
Course Outline
Introduction to LLMs and Agent Frameworks
- Overview of large language models in infrastructure automation.
- Key concepts in multi-agent workflows.
- AutoGen, CrewAI, and LangChain: Use cases in DevOps.
Setting Up LLM Agents for DevOps Tasks
- Installing AutoGen and configuring agent profiles.
- Utilizing the OpenAI API and other LLM providers.
- Setting up workspaces and CI/CD-compatible environments.
Automating Test and Code Quality Workflows
- Prompting LLMs to generate unit and integration tests.
- Using agents to enforce linting, commit rules, and code review guidelines.
- Automated pull request summarization and tagging.
LLM Agents for Alert Handling and Change Detection
- Designing responder agents for pipeline failure alerts.
- Analyzing logs and traces using language models.
- Proactive detection of high-risk changes or misconfigurations.
Multi-Agent Coordination in DevOps
- Role-based agent orchestration (planner, executor, reviewer).
- Agent messaging loops and memory management.
- Human-in-the-loop design for critical systems.
Security, Governance, and Observability
- Managing data exposure and LLM safety in infrastructure.
- Auditing agent actions and restricting scope.
- Tracking pipeline behavior and model feedback.
Real-World Use Cases and Custom Scenarios
- Designing agent workflows for incident response.
- Integrating agents with GitHub Actions, Slack, or Jira.
- Best practices for scaling LLM integration in DevOps.
Summary and Next Steps
Requirements
- Experience with DevOps tools and pipeline automation.
- Working knowledge of Python and Git-based workflows.
- Understanding of LLMs or prior exposure to prompt engineering.
Target Audience
- Innovation engineers and AI-integrated platform leads.
- LLM developers working in DevOps or automation.
- DevOps professionals exploring intelligent agent frameworks.
Open Training Courses require 5+ participants.
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