A technical deep dive into GitHub's Agentic Workflows: how they execute, security architecture, cost models, and practical patterns for continuous issue triage, documentation sync, code review, and CI debugging.
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A deep technical playbook for clearing a stubborn backlog using GitHub Copilot coding agent, with issue templates, instruction patterns, and Python and JavaScript examples.
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Master GitHub Copilot's multiple modes: Ask for understanding, Edit for precise changes, Plan for sequencing complex work, Agent for multi-step execution, and AIAgentExpert for critical tasks. Learn when to use each mode with practical workflows and examples.
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Discover how Agent Mode coordinates multi-step workflows and reduces coordination errors. Learn when to switch from assistant mode to Agent Mode, with real-world use cases for Python, Node.js, and workflow orchestration patterns.
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Discover how to embed Copilot's agentic core directly into your applications. Learn to build intelligent, agent-driven workflows with planning, tool-chaining, execution, and streaming capabilities using the GitHub Copilot SDK.
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Supercharge your developer workflow with GitHub Copilot. Learn the technical differences between Agent Mode and Coding Agent, with step-by-step automation workflows, code samples, and best practices for AI-powered productivity.
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GitHub Copilot Agent Skills let you teach Copilot repeatable, team-specific workflows that go far beyond general-purpose code suggestions. Learn how to structure, implement, and leverage Agent Skills with practical examples.
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Learn how GitHub Copilot Coding Agent on Mobile enables rapid bug resolution from anywhere, transforming incident response with AI-driven code automation and mobile-first DevOps workflows.
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Multi-agent orchestration is revolutionizing how we automate complex, multi-step workflows with AI. Learn how to leverage the Microsoft Agent Framework for constructing resilient, modular agentic workflows and integrate GitHub Copilot for lightweight coding tasks.
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Discover how to seamlessly migrate from Azure DevOps to GitHub with comprehensive technical steps, code examples, pipeline migration strategies, and best practices for modern development workflows.
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Learn how to build robust, production-grade AI agents through advanced evaluation pipelines, feedback loops, and technical best practices using GitHub Copilot and Microsoft Foundry.
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When I develop in GitHub Codespaces, I blend the code-suggesting power of GitHub Copilot with the advanced AI controls of Microsoft AI Foundry. This combination helps me automate routine development tasks and meet enterprise compliance requirements with scalability and precision.
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Instead of automating everything at once, I guide Copilot (and now Copilot agents) through incremental maturity, turning isolated helpers into a reliable team of digital collaborators over time. Here’s a practical guide to that evolution, with samples and observations from the journey.
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Retrieval-Augmented Generation (RAG) actively pulls in relevant information from external data sources to inform Copilot's completions, making your AI business-aware and context-smart.
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I've been diving deep into GitHub Copilot's AgentMode, and it's genuinely a game-changer for how I build, debug, and ship code!
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The increased demand for adaptive, distributed AI solutions has made agentic (multi-agent) architectures a powerful paradigm for scalable automation and intelligent software.
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GitHub Copilot's #CodingAgent is at the core of how I kick off new personal projects(obviously I use a slightly different approach for Enterprise Projects), especially when I start with documentation.
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Learn how to customize GitHub Copilot with Instructions, Prompts, Agents, and Skills to enhance your development workflow.
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GitHub Copilot can be a significant ally in Spec Driven Development, helping you work faster, stay consistent with your specs, and automate large parts of the workflow.
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Spec Driven Development (SDD) is an excellent approach for building a website-whether it's a simple portfolio or a complex web application.
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Issues aren't just for bug reports - they serve as living documents where teams discuss and log decisions, technical debates, and clarifying questions.
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SDD is an approach where specifications - often written as structured documents, test cases, or code-based contracts-drive the implementation of software features.
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Understanding GitHub Copilot's technical safeguards and legal protections will help you establish trust with this powerful AI tool in your organization.
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If you want your developers to move fast but safely then AI policy and governance can't just be an afterthought. Here's a practical blueprint for an internal dev AI policy.
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GitHub Copilot's Agent Mode takes AI assistance to the next level by allowing the AI to understand and execute multi-step instructions, transforming how developers interact with code.
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By letting Copilot handle repetitive code and initial drafts, you can keep your creative momentum up and explore a wider range of solutions in less time.
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Getting sharp code suggestions from GitHub Copilot is no accident - it depends on the context you provide through comments, names, and file structure.
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The spec driven development kit transforms working with requirements from a tedious task into an interactive, creative challenge that makes coding more enjoyable.
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GitHub Copilot helps write code that's easier for teams to collaborate on, with clearer names, better documentation, and consistent formatting.
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Build features fast without compromising security - GitHub Copilot prompts for input validation, edge cases, and security best practices in real time.
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GitHub Copilot helps maintain consistent coding style across projects while catching edge cases you might overlook.
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Review, understand, and validate AI-generated code. Balance the speed of AI assistance with maintaining code quality and safety.
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Pair programming has long been a secret weapon for building stronger skills and catching bugs early. But what happens when your pair is an AI trained on millions of real-world projects?
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Native, two-way integration between GitHub Advanced Security and Defender for Cloud creates an end-to-end feedback loop linking source code findings with runtime security insights.
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