LLMs and Agents in DevOps Workflows Training Course
Large Language Models (LLMs) alongside autonomous agent frameworks such as AutoGen and CrewAI are transforming the way DevOps teams automate tasks like change tracking, test generation, and alert triage by mimicking human-like collaboration and decision-making processes.
This instructor-led live training, available both online and onsite, targets advanced-level engineers looking to design 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 tasks such as test generation, commit analysis, and change summarization using agents.
- Coordinating multiple agents to triage alerts, generate responses, and offer DevOps recommendations.
- Constructing secure and maintainable agent-powered workflows using open-source frameworks.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Customization Options for the Course
- To arrange customized training for this course, please reach out to us.
Course Outline
Introduction to LLMs and Agent Frameworks
- Overview of large language models in infrastructure automation.
- Key concepts in multi-agent workflows.
- Use cases for AutoGen, CrewAI, and LangChain 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
- Addressing 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 leads of AI-integrated platforms.
- LLM developers working within DevOps or automation domains.
- DevOps professionals exploring intelligent agent frameworks.
Open Training Courses require 5+ participants.
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