LLMs and Agents in DevOps Workflows Training Course
Course Outline
Introduction to Large Language Models and Agent Frameworks
- Application of large language models in infrastructure automation
- Foundational principles of multi-agent workflows
- Application of AutoGen, CrewAI, and LangChain for DevOps operations for government
Configuration of LLM Agents for DevOps Functions
- Installation of AutoGen and configuration of agent profiles
- Utilization of OpenAI API and alternative LLM service providers
- Establishment of workspaces compatible with CI/CD pipelines
Automation of Testing and Code Quality Processes
- Employment of prompts to generate unit and integration tests via LLMs
- Use of agents to enforce linting standards, commit protocols, and code review guidelines
- Automation of pull request summarization and classification
LLM Agents for Alert Management and Change Detection
- Design of responder agents to address pipeline failure notifications
- Analysis of logs and traces using language models
- Proactive identification of high-risk changes or configuration errors
Multi-Agent Coordination in DevOps Environments
- Role-based orchestration of agents (planner, executor, reviewer)
- Management of agent messaging loops and memory states
- Implementation of human-in-the-loop controls for critical systems
Security, Governance, and Observability
- Management of data exposure risks and LLM safety in infrastructure
- Auditing of agent actions and enforcement of operational scope restrictions
- Monitoring of pipeline behavior and model feedback loops
Real-World Applications and Custom Scenarios
- Development of agent workflows for incident response
- Integration of agents with GitHub Actions, Slack, or Jira for government workflows
- Best practices for scaling LLM integration within DevOps
Summary and Next Steps
Requirements
- Proficiency in DevOps instrumentation and automated pipeline management
- Competency in Python programming and Git-centric operational workflows
- Familiarity with Large Language Models (LLMs) or practical experience in prompt engineering
Target Audience
- Innovation engineers and platform leadership focused on AI integration
- LLM developers engaged in DevOps practices or automation initiatives
- DevOps specialists investigating intelligent agent frameworks for government applications
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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