How GitHub Copilot Agent transformed my coding workflow

In our sixth #TechTalkThursday, the team walked us through how GitHub Copilot can transform day-to-day development — boosting speed, improving code quality, and enabling developers to work smarter with AI assistance. Here’s what was covered: ⚙️ Copilot setup & activation — how to configure VS Code, enable extensions, sign in via GitHub Enterprise, and tailor settings for your workflow 📊 Usage insights — current adoption vs. available licenses, premium request usage, and why increasing utilization matters 💡 Prompts, instructions & chat modes — understanding how Copilot interprets tasks, follows rules, and executes actions via Ask, Agent, and Inline suggestions 🧠 Vibe Coding (AI-pair programming) — using structured prompts, leaf-node tasks, and clear instructions to get high-quality, safe code from Copilot 🛠️ Advanced tooling — using Copilot CLI in the terminal, auto-generated PR summaries, code translation, and debugging 📦 Reusable prompt & instruction files — creating custom rules, integrating with the “Awesome Copilot” community library, and setting coding standards 🔐 Safe coding practices — protecting credentials, reviewing AI-generated code, validating through unit tests & integration tests The result: Developers can now build features significantly faster, automate repetitive tasks, improve test coverage, and adopt a modern AI-assisted workflow — raising overall productivity across engineering. 👏 Huge thanks to Madasamy M & Naveenkumar S for leading this hands-on deep dive and helping the org adopt Copilot the right way. #TeamTangram #CrayonTechTalks #GitHubCopilot #AIEngineering #DeveloperTools #VibeCoding #AgenticAI

Most developers use GitHub Copilot every day… but many still use its modes randomly. That’s where a lot of productivity gets lost. GitHub Copilot is not just “chat with code”. It has distinct modes; each built for a specific type of work: Ask – when you need explanations, debugging help, or reasoning about code Edit – when you want to modify existing code without rewriting everything Plan – when you’re breaking down large tasks, features, or workflows Agent – when the task is long, complex, and needs autonomous execution Once you start matching the mode to the task, Copilot feels like a completely different tool. Be honest — are you choosing Copilot modes intentionally, or still clicking them randomly? #GitHubCopilot #AI #Development #SoftwareEngineering #ProductivityTools

🚀 GitHub Copilot — Supercharging Developer Productivity! 👨💻🤖 Today, I spent some quality time exploring GitHub Copilot, and I must say, it’s a game-changer for developers and teams building software faster and smarter. 💡 What GitHub Copilot Is GitHub Copilot is an AI-powered coding assistant built by GitHub and OpenAI. It helps you write code faster by suggesting entire functions, completing lines, and even generating tests — all in context as you type. 🔍 Why It Matters Here’s why I believe Copilot is transforming software engineering: ✨ Boosts Productivity – Reduces boilerplate coding and accelerates implementation 🧠 Speeds Learning – Helps you explore unfamiliar frameworks with contextual suggestions 🔁 Improves Consistency – Keeps patterns consistent across teams 🛠 Supports Modern Toolchains – Works with VS Code, and many languages 📌 Real Impact Whether you’re writing APIs, crafting infrastructure scripts, or experimenting with AI/Cloud code — Copilot acts like a 24×7 pair programmer that: ✔ Suggests working code ✔ Reduces repetitive tasks ✔ Saves precious development cycles 🎯 Final Thought AI assistants like GitHub Copilot are not here to replace developers — they’re here to amplify human creativity and productivity. The future of software engineering is collaborative: human + AI. Have you tried Copilot yet? What’s your experience? 🔽 Let’s discuss! #GitHubCopilot #AI #DeveloperTools #SoftwareEngineering #Productivity #MachineLearning

Remember my last post about GitHub Copilot helping me speed up our test automation x4? Well, I finally had my code review 🙂 — and here’s what I’ve learned 👇 I came prepared — waiting for the sounds of excitement and keeping my paper notebook ready to catch insights. What could go wrong? Everything. (just joking 😉 … or not) 💬 How it went In short, the feedback from devs sounded like this: “Not interesting, but OK” “Well done” “Early Junior code — redo as adult” “Bad decision” Previously, I assessed my trust in Copilot at around 15–20%. After this review, I can confidently give it… 25%. 😄 Here’s why 👇 1️⃣ “Not interesting” for devs = gold for me. Copilot’s ability to handle repetitive, templated tasks is actually solid — and mine definitely isn’t. Now I can be sure our automation always has a strong, reliable baseline test suite almost in a whim. 2️⃣ “Well done.” Besides the praise itself, I realized I can already prototype the architecture of testing patterns just with words. That’s my next improvement area — better prompting for technical structure. 3️⃣ “Junior-level code.” Still happy. I’ll rewrite this part manually — and honestly, that’s way more exciting than any abstract coding quiz I do in my QA-to-AQA journey. 4️⃣ “Bad idea.” Fair enough. Some Copilot changes missed crucial corner cases — but even this gave me a benchmark for evaluating our team’s future work with the AI buddy. Four days with Copilot ended like this: ✅ 25% really good ❌ 25% really bad 🚀 50% pure learning Now I’m curious to compare these results with a regular sprint — where I won’t have the luxury of full focus or hackathon mode. ✨ Part 3 is on the horizon. Stay tuned — the real challenge begins when AI joins the sprint. 🌀 Illustration by Leonardo da Vinci

I was working on my app today and paused for a moment after seeing something interesting 👀 GitHub Copilot wasn’t just suggesting code — it was 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗺𝘆 𝗚𝗶𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 using an 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿 𝘃𝗶𝗮 𝗚𝗶𝘁𝗞𝗿𝗮𝗸𝗲𝗻. Here’s what actually happened: Copilot checked the repo status Looked at diffs and logs Staged changes Generated a clear, contextual commit message And committed it — all while explaining what it was doing This made me dig deeper. What’s GitKraken here? GitKraken acts as a Git client + MCP server that exposes Git operations (status, diff, commit, log) in a structured way so tools like Copilot can reason about your repo instead of blindly running commands. Why this matters: This isn’t just “AI writing commits.” It’s AI understanding state, intent, and context across tools. Feels like a glimpse of what developer workflows will look like soon: Less context switching Cleaner commits AI as a teammate, not just autocomplete We’re slowly moving from AI-assisted coding to AI-assisted software engineering.

Beyond speed, GitHub Copilot in AL development means rethinking how we build: moving away from vibe coding towards solid development grounded in architecture. Less improvisation, more method. Here are some ideas : ▶️ Comments that guide Don’t just describe; explain the purpose. Copilot needs context to generate AL code that’s accurate and follows best practices. ▶️ Break down complex tasks. Crawl before you walk. It’s the only way to avoid having the agent "hallucinate" business logic and suggest solutions that don’t fit Business Central. ▶️ Turn ad-hoc into standard. A prompt that works today may break tomorrow. Frameworks like the AL Development Collection help build consistent, reproducible workflows for the whole team. ▶️ Review is part of the design. Copilot speeds things up, but the developer remains the decision-maker. Validation and testing aren’t an afterthought, they’re part of building solid solutions. Success isn’t about merely using AI but designing an environment where agents support our judgment, not replace it. How do you weave AI into your AL workflows? More as a tool or already as a collaborator? #PiensaEnGrande #mvpbuzz #BusinessCentral #GitHubCopilot #msdyn365bc

🚀 GitHub Copilot in IntelliJ: A Tech Lead’s Perspective GitHub Copilot isn’t about replacing engineers, it’s about amplifying team effectiveness when used with the right guardrails. From a tech lead standpoint, here’s where Copilot adds real value: ✅ Reduce cognitive load on boilerplate DTOs, mappers, configs, and test scaffolding get done faster, freeing engineers to focus on design and decisions. ✅ Improve consistency across the codebase Copilot reinforces existing patterns when the codebase is clean and well-structured. ✅ Accelerate refactoring and modernization Quickly convert legacy code, extract methods, and standardize implementations during reviews. ✅ Shift focus to reviews that matter Spend less time on syntax and more on architecture, edge cases, performance, and security. ✅ Great for onboarding, not autopilot Helps new team members ramp up faster , but still requires strong code reviews and clear standards. ⚠️ Velocity without judgment creates tech debt. Copilot is most effective when paired with engineering discipline and ownership. Used intentionally, Copilot in IntelliJ helps teams ship faster without compromising quality. How are you setting boundaries and best practices for Copilot usage in your team? #TechLeadership #GitHubCopilot #IntelliJ #EngineeringExcellence #CodeQuality #AIInEngineering

🚀 GitHub Copilot just leveled up with Agent Skills — a huge step toward AI‑operationalized engineering. Copilot can now learn your workflows, standards, and team knowledge through reusable skills that trigger automatically across the coding agent, Copilot CLI, and VS Code. This means: • Best practices become executable • Consistency scales across teams • Repetitive tasks get automated • Developers focus on higher‑value work For enterprises modernizing on Azure and building AI‑native systems, this is a powerful bridge between documentation and execution. Your engineering culture becomes something Copilot can learn and apply. The future isn’t just AI that assists — it’s AI that carries your team’s craftsmanship forward. #GitHub #Copilot #AgentSkills #AI #DeveloperExperience #CloudNative #Azure #DevOps

Stop the Token Drain. Keep the Vibe Alive with GitHub Copilot. 🎧💸 Are you a "vibe coder"? You know that state—deep work, high momentum, intuitive building. Nothing kills that vibe faster than having to constantly re-explain your entire codebase structure, tech stack, and naming conventions to GitHub Copilot every few turns. It’s frustrating, it breaks your flow, and with large evolving codebases, the token costs are bleeding you dry. We’ve engineered a solution. Introducing the "Project State Extraction" strategy—a deterministic, architectural prompt designed to act as a "Context Firewall" for VS Code Copilot Chat. Instead of repetitive, expensive re-scans of your files, this approach forces the AI to generate a single, high-density compressed "Project Fingerprint." How this changes the game: 🧠 The "AI Onboarding Contract": The prompt instructs Copilot to act as a Principal Architect in READ-ONLY mode. It maps your folder responsibility, detects implicit standards, and defines the rules of engagement once. 📉 Massive Cost Reduction: By feeding subsequent chats this compressed "Fingerprint" instead of raw files, you can drop input token usage by up to 70-90% per request. 🚀 Uninterrupted Flow: You focus on the what (the feature), while the AI perfectly aligns with the how (your established architecture) without guessing. Don't let AI amnesia ruin your coding session. 👇 Check out the visual blueprint below to see how to turn Copilot from a "search and guess" tool into a structured, low-cost architectural partner. #GitHubCopilot #VSCode #AIprogramming #DeveloperProductivity #VibeCoding #SoftwareArchitecture #LLMOptimization

I’ve recently been exploring GitHub Copilot’s Agent Mode in my own workflow, and it’s a huge shift from the traditional snippet-based assistance. Instead of only getting short code completions, Agent Mode actually understands and executes multi-step instructions. This lets me offload bigger tasks, like refactoring a set of related files, scaffolding new project components, or even integrating unit tests with little manual intervention. One of the most powerful aspects I’ve noticed is how essential “context engineering” becomes in determining Copilot’s effectiveness. For example, when I add precise comments, keep my configuration files well-structured, or explicitly outline my intentions before invoking Agent Mode, the outputs become far more on target. This applies especially when working across multiple files or coordinating changes at the architectural level. For instance, by writing a high-level docstring above a function or providing a sample configuration in a README, I’ve watched Copilot assemble workflows that closely mirror my objectives. If I skip or rush context, though, the suggestions tend to become generic or miss finer details. It’s a direct reminder that the quality of AI-driven automation still depends on the quality of the input and structure we provide. From a technical angle, I’ve experimented with custom Copilot extensions, setting up tasks like:- Automated migration of code patterns across an entire repo, using detailed markdown instructions for each desired transformation: - Bootstrapping test suites by documenting expected outcomes along with example input/output pairs - Generating integration scaffolds by outlining data flows and endpoint behavior at the top of a file - My current best practice is to treat Copilot as a collaborator who benefits massively from up-front onboarding: the more I clarify intent, expose key configs, and link relevant files through comments or docs, the more precise and production-ready the generated code becomes. If you’ve been automating complex workflows with Copilot’s Agent Mode or custom extensions, what contexts or documentation tricks made the biggest difference for you? I’m genuinely interested in learning about real-world setups and tips for maximizing output quality. This is a really fun way to explore #AgentMode https://lnkd.in/gsmJba-F #GitHubCopilot #AgentMode #DevExtensions #ContextEngineering #WorkshopPreview Top tip: Agent Mode relies on context that spans multiple files and even project-wide documentation, making it much more effective for sophisticated, multi-step tasks than classic Copilot. Clear intent, structured inputs, and thoughtful prompts let you effectively “program the AI” for workflows that would typically require extensive manual effort.