Ashutosh K. Kandpal’s Post

GitHub Copilot is moving to **usage-based billing starting June 1, 2026** — and this is something organizations should pay close attention to. At first glance, the model seems reasonable: each developer gets a monthly allowance of AI credits, and usage is tied to how much you actually consume. But when you dig deeper, a few concerns start to emerge 👇 🔹 **Unpredictable Costs** Unlike fixed subscription pricing, costs will now vary based on usage patterns. Complex workflows, longer conversations, and agentic features can quickly burn through credits — making it harder for organizations to forecast spend. 🔹 **Agentic AI = Higher Consumption** Modern development is moving toward agent-based workflows (code generation across files, automation, etc.). These are exactly the scenarios that consume *significantly more credits*. Teams adopting advanced AI capabilities may see a sharp rise in costs. 🔹 **Model Selection Becomes a Cost Decision** Engineering teams will now have to think not just about *what works best*, but *what is most cost-efficient*. This introduces a trade-off between performance and budget that didn’t exist before. 🔹 **Hidden Scaling Impact** A few developers experimenting casually? Minimal cost. A full engineering org using Copilot deeply across CI/CD, CLI, chat, and agents? That’s a very different financial story. 🔹 **Shift in Governance Needed** Organizations may now need: * Usage monitoring dashboards * Budget controls per team * Guidelines on when to use which models * Policies around agentic workflows 💭 The bigger question: Are we moving from “AI as a productivity tool” to “AI as a metered infrastructure cost”? For leadership, this isn’t just a billing change — it’s a **FinOps challenge in disguise**. Would love to hear how others are planning to manage this shift. Are you thinking of putting guardrails in place already? https://lnkd.in/dSfj3yce #GitHubCopilot #AIBilling #FinOps #CloudCostManagement #DeveloperProductivity #AIGovernance #SoftwareEngineering #TechLeadership #AgenticAI #AIAdoption #EngineeringManagement #CostOptimization

Spent my weekend “wasting tokens” — turned out to be one of the most practical things I’ve built lately. As a DevOps engineer, I’ve been trying to find a real use case for AI tool (Claude, Kiro, etc) beyond the usual “investigate logs, metrics, events… fix stuff… repeat.” Useful, yes, but not exactly exciting. Then reality hit: PR reviews. Every day: - open browser - check PRs - no notifications about review requests - repeat And the annoying part — if you don’t open the browser, you simply don’t know someone is waiting for your review. So I decided: what if I just… don’t open the browser? 👉 https://lnkd.in/dhe6ifEA Built a small macOS status bar app using Claude Code from Anthropic that: - shows updates on PRs assigned to me (approved, changes requested, etc.) - notifies when someone requests my review - basically nags me so I don’t have to rely on manually checking GitHub It’s not perfect yet: - needs signing certificate - brew integration would be nice - auto-start is still on the list But honestly? It already makes my workflow noticeably smoother. Big realization for me: even when “everything is already built,” there’s still a ton of personal friction you can remove. And AI tools like Claude Code are surprisingly good at helping you build those small, very specific things you didn’t know you needed. Turns out the best use case wasn’t replacing my work — it was removing the annoying parts of it. Curious how others are using Claude Code beyond debugging / analysis? #ClaudeCode #AI #Anthropic #GitHub #DevOps

Tool limitations aren't roadblocks—they’re architectural challenges. I’ve learned over the years that lacking the "perfect" budget or the latest proprietary equipment doesn't stop you from delivering high-level work. In fact, it usually makes you a better engineer. When my hardware was outdated, I didn't wait for an upgrade. I optimized. I shifted my workflow to the cloud—leveraging VSLS, Gitpod, AWS Cloud9, and JetBrains Space—while keeping my local Linux environment lean and mean for the essentials. When StackOverflow felt too slow, I didn't just complain about the "stone ages." I built a custom Python tool to scrape and synthesize solutions using AI long before it was a built-in feature in every IDE. The Shift: From Coding to Architecting 🏗️ Now, as AI agents and LLMs take the industry by storm, the game has changed again. But the lesson remains the same: Prompt Engineering and Context Management are the new vital skills. Software Foundations are more important than ever. This is what separates the Architects from the "human-equivalent of a typewriter." Anyone can generate code now, but only an engineer with a foundational mindset knows how to structure it for longevity and security. Independence > Dependency When tools like Claude Code or expensive agentic systems launch, I don't feel the "need" to pay for them immediately. Why? Because the optimization skills I developed during my budget-conscious years allow me to harness the power of new models without being dependent on them. Whether it’s GitHub Copilot, Gemini Code Assistant, or a custom-built script, the environment is just a variable. My "default" is efficiency and problem-solving. When your engineering foundation is solid, moving between tools isn't a challenge—it’s trivial. Efficiency is a mindset, not a subscription. I’m curious—how has your journey through different "tool eras" shaped the way you code today? Let’s talk in the comments. 👇🏼 #SoftwareEngineering #CleanArchitecture #AI #CloudComputing #FoundationalEngineering #CareerGrowth #TechMindset

Agentic workflows and parallelised reasoning sessions are demanding so much processing power that GitHub is restricting Copilot Individual plans. New sign-ups are paused, and strict token-based usage caps are being enforced directly inside VS Code and the CLI. Will your engineering team need to adjust its CI/CD pipelines and daily coding habits? https://lnkd.in/eCUQiAeY #github #copilot #agenticai #developers #ai #softwaredevelopment #technology

##GitHub just changed the way we build software, and most people are still sleeping on it. There’s a new tab quietly rolling out in repositories: 👉 “Agents” And it signals something big. We’re no longer just using AI to write code. We’re starting to delegate engineering work to it. Let that sink in. Not: “write this function” But: “fix this bug, run tests, and open a PR” And an AI agent actually goes and does it. In the background. End-to-end. The new GitHub Copilot Agent workflow is shifting development from: 🧠 Human writes code 🤖 AI assists to: 🧠 Human defines task 🤖 AI executes task The “Agents” tab is basically: • A control panel for AI workers inside your repo • A place to track what your AI is doing • A dashboard for autonomous dev workflows • A bridge between ideas and working pull requests And here’s the uncomfortable truth: We are moving from coding as execution to coding as direction. The best engineers in this new world won’t be the ones who type the fastest. They’ll be the ones who can: • break problems down clearly • define instructions precisely • manage AI systems like teammates Software engineering is quietly shifting into something new: 👉 from writing code 👉 to orchestrating agents that write it for you And GitHub didn’t announce it loudly. They just shipped it. We’re not in “AI-assisted development” anymore. We’re entering AI-operated development. The question is no longer: “Can you code?” It’s: “Can you lead machines that code?” Because the future repo won’t just have developers. It will have agents working alongside them. #AI #Agent #Devs #github #git #tech #learners #update #trending #programming #backend #frontend #mobile #machinelearning #ml

🚀 GitHub Copilot Workspace – The Future of AI-Driven Development? Let’s be honest—most “AI in development” tools promise more than they deliver. But what’s interesting about GitHub Copilot Workspace is not just code suggestions—it’s the shift in how we approach building software. As a Senior Full Stack Developer working on Java microservices and large-scale distributed systems, here’s my practical take 👇 🔍 What’s actually different? Traditional tools like GitHub Copilot help you write code faster. But Workspace tries to: Understand the entire problem Generate a plan + code Let you iterate at a system level, not just line-by-line 👉 That’s a big shift—from coding assistant → engineering assistant 💡 Where it actually helps (real scenarios) From my experience using AI tools in production environments: ✅ Boilerplate-heavy microservices Creating Spring Boot services, DTOs, controllers Reduces repetitive setup time significantly ✅ API-first development Quickly scaffolds REST endpoints + validation layers Helps standardize patterns across services ✅ Refactoring legacy code Suggests improvements for readability and structure Especially useful in large codebases ⚠️ Where it still struggles Let’s not overhype it. ❌ Complex business logic Domain-heavy systems (payments, banking rules) still need human thinking ❌ System design decisions It won’t replace experience in designing scalable architectures ❌ Production debugging Logs, distributed tracing, real-world failures → still manual expertise 🧠 What this means for developers The real change isn’t “AI will replace developers.” It’s this: 👉 Developers who use AI effectively will replace those who don’t The skill shift is clear: Less time writing boilerplate More time on architecture, design, and problem-solving 🔮 My takeaway Tools like Copilot Workspace are not magic. But they are moving us toward a new development model: 👉 From writing code → orchestrating systems with AI assistance And honestly, that’s where senior engineers should already be focusing. Curious to hear from others— Are you using AI tools in your daily workflow, or still skeptical? #Java #FullStack #Microservices #AI #GitHubCopilot #SoftwareEngineering #DevOps #Cloud

I’ve been thinking a lot about how AI agents are starting to feel less like “tools” and more like real software components. We’re versioning them, reviewing them, shipping them and treating them as part of the engineering workflow. I pulled those thoughts together into a short post on what it means to treat agents as code and why this shift matters for teams building with AI today. Read it here: https://lnkd.in/g2Ff7iBb #AgenticDevOps #AIEngineering #Agents #GitHub #Copilot

This article explores how Drasi leveraged GitHub Copilot CLI and AI agents to automatically test tutorials, prevent documentation drift, and enhance open-source onboarding. I found it interesting that AI can not only assist in coding but also improve the quality of documentation, which is crucial for developers. What are your thoughts on the role of AI in software documentation?

GitHub Copilot just changed the rules. Not just for developers. For engineering leaders. The shift to token-based billing isn’t just a pricing update, it’s a signal. AI tools are growing up. And with that comes a new set of responsibilities most teams aren’t ready for: → Usage visibility (who’s using what, and how much?) → Cost governance (is this ROI-positive or just noise?) → Smarter engineering decisions (quality over quantity of AI calls) For years, the conversation was about adoption. “Get your devs on Copilot.” “Increase AI usage.” “Move fast.” Now the conversation shifts to: Are you using it wisely ? The teams that win the next phase of AI tooling won’t just be the ones who adopted early. They’ll be the ones who governed intelligently. Is your org ready for that conversation ? #GitHub #Copilot #AITooling #EngineeringLeadership #DevOps #TechStrategy #PlatformEngineering #SoftwareEngineering #ADLC

🤖 How I Use AI Agents + GitHub Copilot to Ship Better Code, Faster After months of experimenting, I’ve built a multi-agent workflow that’s genuinely changed how I work as a software engineer. Here’s my exact process: Before touching any code, I read the codebase carefully. AI is only as good as the context you give it — this step is non-negotiable. Step 1 — Planning Agent (Claude Opus) I pass the user story + my codebase analysis to a planning agent with detailed instructions. It drafts a precise implementation plan. I review it, refine it if needed, then approve. Step 2 — Implementation Agent (Claude Sonnet) The approved plan goes to a dedicated implementation agent. I review every change it makes and apply improvements where needed. Human judgment stays in the loop. Step 3 — Test Planning Agent (Claude Opus) I instruct this agent to use the git diff tool to read the latest changes, search the codebase for similar test patterns, and draft a test plan. Same review-then-execute flow as before. Step 4 — Test Implementation (Claude Sonnet) The implementation agent writes the tests against the approved plan. I go through every single test, read it carefully, debug, and adjust. Step 5 — Code Review Agent (Claude Opus) Before creating the MR, a final agent reads the full diff and performs a code review. I address any critical findings before pushing. The result? I’m more productive and more reliable than before. The key insight: I’m not delegating thinking — I’m delegating execution. I spend more time on architecture, best practices, and actually understanding the product. The time-consuming implementation work? That’s on the agents. Multi-agent workflows aren’t about replacing engineering judgment. They’re about amplifying it. 🚀 #SoftwareEngineering #AIAgents #GitHubCopilot #DeveloperProductivity #ClaudeAI #AIDrivenDevelopment