LLMs and Agents in DevOps Workflows

• Experience with DevOps tooling and pipeline automation
• Working knowledge of Python and Git-based workflows
• Understanding of LLMs or exposure to prompt engineering

Audience

• Innovation engineers and AI-integrated platform leads
• LLM developers working in DevOps or automation
• DevOps professionals exploring intelligent agent frameworks

LLMs and autonomous agent frameworks like AutoGen and CrewAI are redefining how DevOps teams automate tasks such as change tracking, test generation, and alert triage by simulating human-like collaboration and decision-making.

This instructor-led, live training (online or onsite) is aimed at advanced-level engineers who wish to design and implement DevOps automation workflows powered by large language models (LLMs) and multi-agent systems.

By the end of this training, participants will be able to:

• Integrate LLM-based agents into CI/CD workflows for smart automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Coordinate multiple agents for triaging alerts, generating responses, and providing DevOps recommendations.
• Build secure and maintainable agent-powered workflows using open-source frameworks.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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
• Using 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

• Handling 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