Description

Introduction

Software development and DevOps are rapidly being transformed by agentic AI, where autonomous agents plan, execute, and iterate tasks such as code generation, testing, deployment, and monitoring. This course focuses on how AI agents—powered by LLMs and frameworks like LangChain, AutoGen, and CrewAI—can be applied across the software development lifecycle (SDLC) and DevOps pipelines to enhance velocity, consistency, and resilience.

Prerequisites

This course is designed for software engineers, DevOps professionals, and tech leads with:

• Proficiency in Python and shell scripting

• Familiarity with Git, CI/CD tools (GitHub Actions, Jenkins, etc.)

• Understanding of basic AI/ML concepts and LLMs

• Optional: Experience with infrastructure-as-code (IaC), observability tools, or testing frameworks

Table of Contents

1. Introduction to Agentic AI for Dev Engineering
1.1 What is Agentic AI in the DevOps and Software Context?
1.2 Benefits over Scripted Automation and Chatbots
1.3 Overview of Tools: LangChain, AutoGen, CrewAI, OpenDevin

2. Software Development with Autonomous Agents
2.1 Code Generation and Refactoring Agents
2.2 Bug Detection and Auto-Remediation Workflows
2.3 Review Agents for Pull Request Summarization and Linting
2.4 Documentation and Commenting Bots with Natural Language Understanding

3. Test Automation and Validation
3.1 Agentic Testing for Unit, Integration, and Regression
3.2 Writing and Updating Test Cases Automatically
3.3 CI Agents for Pipeline Health Checks and Debugging
3.4 AI-Powered Load Testing and Performance Analysis

4. DevOps and Infrastructure Automation
4.1 Deployment Agents with Change Management Logic
4.2 Agentic Monitoring for Log Analysis and Incident Triage
4.3 Auto-Healing Systems with Plan-and-Act Loops
4.4 Managing IaC with Autonomous GitOps Agents

5. LangChain, AutoGen & CrewAI for Dev Workflows
5.1 Building ReAct-based Agents to Navigate Repos and APIs
5.2 LangChain Tool Usage: Git CLI, Docker, Kubernetes
5.3 AutoGen Role-Based Teams for Dev-Test-Deploy Pipelines
5.4 CrewAI for Managing Multi-Agent Collaboration Across Environments

6. Secure and Responsible Deployment
6.1 Auditability and Explainability in Engineering Decisions
6.2 Securing Access to CI/CD, Secrets, and Repos
6.3 Implementing Approval Steps and Guardrails
6.4 Aligning Agents with DevSecOps Practices

7. Case Studies and Real-World Applications
7.1 AutoPR: Autonomous Feature Completion to PR Submission
7.2 AI Ops Bots for Downtime Prediction and Alert Enrichment
7.3 SRE Assistant: LLM-Based Postmortem and RCA Generator
7.4 Enterprise Dev Assistants: IDE Plug-ins and CLI Agents

Agentic AI is revolutionizing software engineering and DevOps, enabling intelligent agents to assist, accelerate, and autonomously manage various stages of SDLC and operations. By the end of this course, you’ll understand how to design, deploy, and scale LLM-driven AI agents that integrate with existing developer tools and DevOps stacks—paving the way for more agile, resilient, and intelligent engineering workflows.