What does agentic AI in software development mean in this course?

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.

When would you use agentic AI in a software workflow?

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.

How does agentic AI fit into a broader software delivery workflow?

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.

How is agentic AI in software development different from traditional automation?

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.

Do you need any prerequisites before learning agentic AI for software workflows?

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.

What tools, platforms, or methods are used in this course?

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.

What specific tasks will you practice or complete in this course?

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.

Agentic AI Content for Practitioners: Software

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Agentic AI Content for Practitioners: Software

Dozent: Hurix Digital

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3 Module

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3 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

4 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Kompetenzen, die Sie erwerben

Kategorie: Technology Strategies
Kategorie: Risk Management Framework
Kategorie: Test Automation
Kategorie: Maintainability
Kategorie: Scalability
Kategorie: Application Deployment
Kategorie: CI/CD
Kategorie: DevOps
Kategorie: AI Integrations
Kategorie: Continuous Monitoring
Kategorie: Generative AI Agents
Kategorie: Agentic systems
Kategorie: Continuous Deployment

Werkzeuge, die Sie lernen werden

Kategorie: LangGraph
Kategorie: GitHub Copilot
Kategorie: LangChain
Kategorie: Agentic Workflows
Kategorie: AI Workflows

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In diesem Kurs gibt es 3 Module

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.

Moduldetails

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.

Das ist alles enthalten

4 Videos2 Lektüren1 Aufgabe

4 VideosInsgesamt 20 Minuten

Introduction and Welcome4 Minuten
What Makes AI "Agentic" in Software Development?5 Minuten
LangChain and LangGraph: The Foundation of Agentic AI6 Minuten
Real-World Success Stories: Agentic AI in Testing5 Minuten

2 LektürenInsgesamt 14 Minuten

Welcome to the Course: Course Overview4 Minuten
Framework Implementation Strategies for Test Automation10 Minuten

1 AufgabeInsgesamt 20 Minuten

HOL: Design Your Agentic AI Testing Framework20 Minuten

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.

Das ist alles enthalten

3 Videos1 Lektüre1 Aufgabe

3 VideosInsgesamt 19 Minuten

GitHub Copilot's Evolution: From Assistant to Agent5 Minuten
Technical Deep Dive: AI Agent Integration Patterns8 Minuten
Case Study: Successful AI Agent Integration in Production6 Minuten

1 LektüreInsgesamt 6 Minuten

Microsoft's Agentic AI Vision for Software Development6 Minuten

1 AufgabeInsgesamt 15 Minuten

HOL: Design a CI/CD Integration Strategy for AI Agents15 Minuten

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.