DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

How does AI transform chaos engineering from an experiment into a critical capability? Learn how to effectively operationalize the chaos.

Data quality isn't just a technical issue: It impacts an organization's compliance, operational efficiency, and customer satisfaction.

Are you a front-end or full-stack developer frustrated by front-end distractions? Learn to move forward with tooling and clear boundaries.

Developer Experience: Demand to support engineering teams has risen, and there is a shift from traditional DevOps to workflow improvements.

Related

  • RAG vs. CAG: A Deep Dive into Context-Aware AI Generation Techniques
  • Traditional Testing and RAGAS: A Hybrid Strategy for Evaluating AI Chatbots
  • Code Reviews: Building an AI-Powered GitHub Integration
  • The Role of Retrieval Augmented Generation (RAG) in Development of AI-Infused Enterprise Applications

Trending

  • Understanding the Circuit Breaker: A Key Design Pattern for Resilient Systems
  • Before You Microservice Everything, Read This
  • Understanding the 5 Levels of LeetCode to Crack Coding Interview
  • Memory Leak Due to Uncleared ThreadLocal Variables
  1. DZone
  2. Data Engineering
  3. AI/ML
  4. GitHub Copilot's New AI Coding Agent Saves Developers Time – And Requires Their Oversight

GitHub Copilot's New AI Coding Agent Saves Developers Time – And Requires Their Oversight

GitHub has launched a powerful AI coding agent in Copilot that writes code, fixes bugs, and opens pull requests.

By 
Aminu Abdullahi user avatar
Aminu Abdullahi
·
May. 22, 25 · News
Likes (2)
Comment
Save
Tweet
Share
4.4K Views

Join the DZone community and get the full member experience.

Join For Free

At Microsoft’s Build developer conference, GitHub announced the rollout of a new AI coding agent built directly into GitHub Copilot. This upgraded assistant can now handle development tasks like fixing bugs, writing features, refactoring code, and improving documentation.

Developers can assign issues to Copilot through GitHub.com, GitHub Mobile, or the GitHub command-line interface, just like assigning them to a human. The agent reacts with an eyes emoji and kicks off its work.

Behind the scenes, it boots up a virtual machine, clones your repo, configures its environment, and starts reading your codebase. It uses retrieval augmented generation (RAG) and GitHub code search to understand what’s going on and what it needs to do.

Built-in Security, and Human Oversight Required

Despite all its power, the Copilot agent doesn’t act without checks. Every change it makes goes into a separate branch and draft pull request; it won’t push code live without human review and can’t approve its own work.

“The agent can only push to branches it created, keeping your default branch and the ones your team created safe and secure,” Thomas Dohmke, chief executive officer of GitHub, wrote in a blog post.

You’ll see its reasoning through session logs, which trace every decision it makes. Developers can also leave comments on pull requests, and Copilot will respond by editing the code accordingly.

GitHub’s agent is tightly integrated with GitHub Actions, the company’s CI/CD platform that runs more than 40 million daily jobs. That means the agent works within your current workflow, not outside of it.

And with the Model Context Protocol (MCP), it can use external data sources and follow custom repository instructions, helping it stay aligned with your team’s coding style and intent.

Availability and Cost

The agent is currently in preview and only available to users of Copilot Pro+ and Copilot Enterprise.

To activate it, you’ll need to enable it in your repository settings. For Enterprise users, admins must turn on the policy. Starting June 4, 2025, using the coding agent will cost one premium request per model call.

GitHub says the agent is available on GitHub’s website, mobile app, CLI, and more IDEs, including Xcode, Eclipse, JetBrains, and Visual Studio.

GitHub Levels Up in the AI Coding Race

With the debut of its AI agent, GitHub is stepping into a more competitive space. Other tech giants have also entered the AI coding agent race: Google unveiled Jules last year, and OpenAI recently showcased its Codex agent.

Still, GitHub’s advantage lies in its seamless integration into the everyday developer workflow. And with over 15 million Copilot users and counting, the company hopes to redefine what it means to work with AI in software development.

AI GitHub RAG

Published at DZone with permission of Aminu Abdullahi. See the original article here.

Opinions expressed by DZone contributors are their own.

Related

  • RAG vs. CAG: A Deep Dive into Context-Aware AI Generation Techniques
  • Traditional Testing and RAGAS: A Hybrid Strategy for Evaluating AI Chatbots
  • Code Reviews: Building an AI-Powered GitHub Integration
  • The Role of Retrieval Augmented Generation (RAG) in Development of AI-Infused Enterprise Applications

Partner Resources

×

Comments

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • [email protected]

Let's be friends: