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⇱ Agentic AI Content for Practitioners: Software | Coursera

Agentic AI Content for Practitioners: Software

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Agentic AI Content for Practitioners: Software

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

There are 3 modules in this course

Optimize Software Development with Agentic AI is an intermediate-level course designed for software developers, DevOps engineers, and technical leaders who want to harness the power of autonomous AI agents in their development workflows. As Gartner predicts that 33% of enterprise software applications will include agentic AI by 2028, this course provides the strategic foundation and practical skills needed to implement AI agents successfully. You'll master frameworks like LangChain and LangGraph for test automation, learn to integrate AI agents with GitHub Copilot in CI/CD pipelines, and develop comprehensive deployment strategies that avoid the common pitfalls causing 40% of agentic AI projects to fail. Through real-world case studies from Microsoft, McKinsey, and leading tech companies, hands-on exercises, and interactive coaching, you'll build the expertise to transform your development processes with intelligent automation. Whether you're optimizing testing workflows, enhancing CI/CD pipelines, or building resilient DevOps operations, this course equips you with the knowledge and tools to lead the next generation of AI-enhanced software development.

This foundational lesson introduces learners to agentic AI in software development, focusing on frameworks like LangChain and LangGraph for test automation. Learners will explore how autonomous AI agents can revolutionize testing processes, examine real-world implementations, and understand the strategic considerations for successful adoption in development teams.

What's included

4 videos2 readings1 assignment

4 videos•Total 20 minutes
  • Introduction and Welcome•4 minutes
  • What Makes AI "Agentic" in Software Development?•5 minutes
  • LangChain and LangGraph: The Foundation of Agentic AI•6 minutes
  • Real-World Success Stories: Agentic AI in Testing•5 minutes
2 readings•Total 14 minutes
  • Welcome to the Course: Course Overview•4 minutes
  • Framework Implementation Strategies for Test Automation•10 minutes
1 assignment•Total 20 minutes
  • HOL: Design Your Agentic AI Testing Framework•20 minutes

This lesson focuses on the strategic integration of AI agents with GitHub Copilot in continuous integration and deployment pipelines. Learners will explore how to leverage Microsoft's latest agentic AI capabilities, understand the technical considerations for seamless integration, and design workflows that enhance code quality while maintaining deployment velocity.

What's included

3 videos1 reading1 assignment

3 videos•Total 19 minutes
  • GitHub Copilot's Evolution: From Assistant to Agent•5 minutes
  • Technical Deep Dive: AI Agent Integration Patterns•8 minutes
  • Case Study: Successful AI Agent Integration in Production•6 minutes
1 reading•Total 6 minutes
  • Microsoft's Agentic AI Vision for Software Development•6 minutes
1 assignment•Total 15 minutes
  • HOL: Design a CI/CD Integration Strategy for AI Agents•15 minutes

This final lesson focuses on the strategic deployment and long-term management of AI agents in DevOps environments. Learners will explore deployment strategies, monitoring approaches, and team adoption methodologies. The lesson emphasizes building resilient, scalable AI-enhanced DevOps practices while avoiding common pitfalls that lead to project failure.

What's included

4 videos1 reading3 assignments

4 videos•Total 20 minutes
  • Beyond Implementation: Strategic Deployment of AI Agents•5 minutes
  • Avoiding the Failure Trap: Common Pitfalls in AI Agent Deployment•5 minutes
  • Building Resilient AI Agent Operations•8 minutes
  • Congratulations and Continuous Learning Journey•2 minutes
1 reading•Total 15 minutes
  • Strategic Deployment Frameworks for Enterprise AI Agents•15 minutes
3 assignments•Total 70 minutes
  • HOL: Design a Complete AI Agent Deployment Strategy•15 minutes
  • Project: Agentic AI DevOps Optimization Portfolio•45 minutes
  • Assessment•10 minutes

Instructor

454 Courses•58,950 learners

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Frequently asked questions

In this course, agentic AI means using autonomous AI agents as active parts of software work rather than as simple code suggestions or fixed scripts. The focus is on how those agents support testing, CI/CD, and DevOps workflows by using context, taking focused actions, and learning from results.

You would use it when testing, build validation, or deployment work involves repeated decisions that fixed automation handles poorly. The course focuses on situations where AI agents can respond to code changes, pipeline events, and feedback instead of only running predefined steps.

It fits into the build-and-test phase and extends into CI/CD and DevOps as a connected layer of analysis, action, and follow-up. In this course, agentic AI is treated as part of a repeatable delivery process, not just a one-off assistant used while writing code.

Traditional automation follows predefined rules, while agentic AI is taught here as a system that can use context, adapt its next step, and coordinate specialized roles. The course emphasizes that difference in testing and pipeline work, where fixed scripts often need more manual intervention when conditions change.

A basic understanding of software development practices, CI/CD concepts, version control, and automated testing is helpful. This is an intermediate course, so it assumes you already recognize the workflow and want to learn how agentic AI fits into it.

The course uses agent frameworks such as LangChain and LangGraph, and it also looks at how AI agents work alongside GitHub Copilot in CI/CD pipelines. Method-wise, the emphasis is on multi-agent orchestration and event-driven workflow integration.

You will practice analyzing code changes, defining specialized agent roles, generating and prioritizing tests, and connecting agents to CI/CD events and build decisions. You also work on monitoring, feedback, and human handoff points so agentic AI can support software delivery in a controlled, repeatable way.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.