GitHub Copilot Agents Need Governance

This repo introduces simple, powerful, and architecture-first strategy for structuring Copilot agent projects based on best AI engineering practices - including plan first, delegate to sub-agents, pursue elegant form, fix what’s broken, validate everything, learn from mistakes - a truly fantastic template for reproducible agentic configuration 🤖⚙️! https://lnkd.in/dGHszBdt #githubCopilot #agentArchitecture #agenticAI #projectStructure

Most people use GitHub Copilot the wrong way. They treat it like autopilot. That’s why they end up with messy, unreliable code. Here’s the truth: Copilot is not a replacement for thinking — it’s a force multiplier. If you use it right, it can 10x your productivity. Here’s how I use it to get robust results: - Treat it like a junior developer Give clear instructions. Don’t expect magic from vague prompts. - Use comment-driven development Write structured comments first → let Copilot generate the code. - Break problems into small chunks Big tasks confuse it. Smaller steps = cleaner output. - Always review and refactor Never blindly accept suggestions. Validate logic, handle edge cases. - Use it to write tests One of the most underrated use cases. Great for covering edge scenarios. - Be explicit about constraints Mention frameworks, libraries, and rules. Otherwise, it guesses. - Use it for patterns, not decisions Let it handle boilerplate — you handle architecture. - Iterate, don’t settle The first suggestion isn’t always the best. Guide it to improve. My workflow: → Define with comments → Generate with Copilot → Review & refine → Add tests → Improve structure The result? Faster development without sacrificing quality. Bottom line: AI won’t replace developers — but developers who use AI well will replace those who don’t. #AI #GitHubCopilot #SoftwareEngineering #Productivity #Developers #VibeCoding

I'll be honest—I dismissed this feature too quickly. When GitHub Copilot CLI introduced the /pr create and /pr fix commands, my initial reaction was "why bother? I can just prompt it to create a PR." Here's what I missed: PR create doesn't just open a pull request. It handles the entire pre-flight checklist—ensuring your branch is synced, rebased, and ready. No more "your branch is X commits behind" surprises. PR fix takes it even further. It monitors your CI pipelines, identifies failures, applies fixes, and resolves merge conflicts automatically. The entire feedback loop that used to eat 20+ minutes? Handled. This is part of a larger shift I'm seeing: AI tools are moving from "helpful suggestions" to "autonomous workflow automation." The commands that seem redundant at first glance often hide the most powerful capabilities. For engineering leaders: this is worth evaluating for your team's developer experience metrics. The cumulative time savings on PR hygiene alone is substantial. Full breakdown of 5 essential Copilot CLI commands in my latest video—covering MCP management, skills, agent browsing, and more. What's a dev tool feature you initially dismissed but now can't live without? #DeveloperProductivity #GitHubCopilot #DevOps

Biggest week for GitHub Copilot's coding agent since launch — 5 updates in 4 days. Here's what enterprise teams need to know: 🔍 Semantic code search — the agent now finds code by meaning, not exact text. Better context discovery across large monorepos. ⚡ 50% faster startup — across issue assignment, Agents tab, and PR comment invocations. Makes the agent viable for smaller, time-sensitive tasks. 🔒 Configurable validation — admins can toggle CodeQL, Advisory Database, secret scanning, and Copilot code review per-repo. All free. No Advanced Security license required. 📋 Commit-level audit trails — every agent commit now includes an Agent-Logs-Url trailer linking directly to the full session log. Click any agent-generated commit → see exactly what it did and why. This is the governance story enterprises have been waiting for. 📡 Enhanced session visibility — setup steps, subagent activity, and live status now surface directly in the session view. Raycast extension adds live log streaming. On the model side: → GPT-5.3-Codex is GitHub's first LTS model (guaranteed through Feb 2027). Auto-rollout deadline: May 17, 2026. If your org hasn't reviewed it yet, start now. → GPT-5.4 mini is GA across all tiers at a 0.33x premium request multiplier. Enterprise admins need to explicitly enable it. VS Code 1.112 also shipped with MCP server sandboxing, a new /troubleshoot command for debugging agent logs, and Autopilot mode hitting Stable. Edit Mode is deprecated — full removal in v1.125. Three dates for your calendar: 📅 May 17 — GPT-5.3-Codex auto-rollout 📅 v1.125 — Edit Mode fully removed 📅 Weekly — VS Code now ships stable releases every week Full writeup on the blog: https://lnkd.in/gQqi_Dsy #GitHubCopilot #CopilotCodingAgent #AIDevTools #EnterpriseAI #VSCode #DeveloperProductivity

STOP saying your team knows GitHub. Let’s be honest. If your team is only: → Pushing code → Creating pull requests → Merging branches That’s NOT “knowing GitHub.” That’s just… basic usage. Here’s what most teams are missing: ❌ No automation (everything manual) ❌ No AI usage (slow development) ❌ No security checks (risky deployments) ❌ No structured workflows (messy collaboration) 💡 Meanwhile, high-performing teams are: ✔ Automating everything with GitHub Actions ✔ Writing code faster using Copilot ✔ Catching vulnerabilities early ✔ Shipping faster with fewer errors 👉 Same tool. 👉 Completely different outcomes. At Evolvv, we help teams move from: “Just using GitHub” → to → “building high-performance engineering workflows.” 🔥 If your team is still at the basic level, you’re already behind. #Evolvv #GitHub #DevOps #AI #GitHubCopilot #Automation #SoftwareDevelopment #TechTeams #Upskilling #EngineeringLeadership

"The architecture behind why this scales." 🚨 Most people think skills.md is just a long prompt. It's not! And understanding why changes how you build for scale. Anthropic's engineering team called the principle behind it: "Progressive Disclosure" Here's how it works: 🔍 Level 1 — Discovery → Agent pre-loads only the skill name and description → Just enough to know the skill exists and when to use it ⚡ Level 2 — Activation → Task matches? Full skills.md loads into context → Only then. Only if relevant. 📚 Level 3 — Deep Context → Skill references examples, templates, edge cases? → Agent reads those too — only what it needs, only when needed The result: Context is effectively unbounded — because none of it loads until it's needed. Anthropic's own analogy: "Like a well-organised manual — table of contents first, then specific chapters, then the detailed appendix." This is why the Dojo's Six Disciplines don't bloat the context window. Behavioral contracts load on demand — not all at once. For enterprise codebases, this means you can encode: 🛡️ Security review standards 🏗️ Architecture decision rules 🧪 Team-specific testing conventions 📖 Lessons from past incidents All in one skills.md None of it wastes context until the task actually needs it. Most teams write one flat prompt and wonder why the agent ignores half of it. The answer isn't a better prompt. It's a better structure. 🗂️ Layer 1 — name + description (always loaded) 📋 Layer 2 — core instructions (loaded when relevant) 🔗 Layer 3 — references, examples, edge cases (loaded on demand) That's the architecture behind the Dojo. That's why it scales. Where should I start? 👉https://lnkd.in/gQizUTUJ Next post → How to roll this out across your enterprise. The operating model. #GitHubCopilot #AgenticAI #EngineeringLeadership #Microsoft #AIArchitect #DevEx

Is your enterprise struggling to decide which AI-based IDE to rollout to the developer community? You are not alone. Everyone is debating "Claude Code vs Codex vs Copilot and yes good old Cursor." But AWS just shipped something that changes the conversation entirely — and most teams haven't caught up yet. I spent the last week doing a deep review of three tools: AWS Kiro, Claude Code, and GitHub Copilot. I used to use Cursor but now settled on Claude Code and GHCP, but Kiro was new to me. So I did a deep dive and the analysis led to crucial insights: → Which tool trains on your code? (Spoiler: one of them just changed its policy — effective April 24) → What does a 10-engineer team actually spend per month — not list price, real cost? → Which tool survives contact with a 500k-line legacy codebase? → What happens when an agent inherits your production IAM credentials and gets it wrong? The answers surprised me. And the right answer isn't "pick one" — it's understanding exactly which tool wins which battle. Key findings: 🟡 Kiro is the only tool that won't write a line of code without your sign-off on a formal spec. For greenfield enterprise projects, that's a game-changer. For brownfield maintenance, it's friction. 🟣 Claude Code has a 1M token context window in beta. Feed it your entire legacy codebase. It reads it. That's gold for brownfield application refactoring. 🔵 GitHub Copilot just became model-agnostic — GPT-5, Claude, Gemini, all in one interface. And at $19/user/mo, it's the only tool most enterprises can realistically roll out to every developer. My findings including data privacy matrix, real-world cost benchmarks, agentic safety analysis, vendor lock-in risk, and a practical replication guide for getting Kiro's best features in Claude Code and Copilot — is on my Medium blog. Read on and tell me what your team is using for development in the comments. #AgenticAI #SoftwareEngineering #AWSKiro #ClaudeCode #GitHubCopilot #EnterpriseAI #DevTools #AIProductivity #SignalOverNoise

Where do autonomous agents create the most value first - and where are the hidden risks? GitHub Copilot's coding agent is already taking issues, writing code, running tests, opening PRs and iterating - asynchronously - while you focus on architecture. That's not pair programming anymore. That's a peer programmer. GitHub calls it the "from pair to peer" shift and the numbers back it up: 55% faster task completion, with agents removing the "waiting for a human to pick it up" lag entirely. Beyond dev, value lands first where work is: High volume and repetitive (compliance checks, dependency patching, PR reviews) Well-structured with clear success criteria (test generation, security scanning) Expensive when slow (incident response, engineer onboarding) But here's where the conversation got interesting. The risks are NOT where most people look. It's not the agent "going rogue." It's three quieter things: Prompt injection at scale. Agents reading PRs, issues and external repos can be manipulated by malicious content embedded in those sources. Microsoft's Security team specifically called this out for autonomous agents. We are not talking about it enough. Accountability diffusion. When an agent writes the code, who owns the bug? The dev who approved the PR? The platform? The model? Nobody has actually resolved this yet - legally or organisationally. Automation bias. Humans rubber-stamping agent output because it looks confident. The trust calibration problem is real and it is growing faster than the tooling designed to address it. The productivity gains are real. The risks are also real. The teams winning with agents are the ones building both into their thinking from day one. https://msft.it/6040Qtzsk What are you seeing in your teams? #AI #GitHubCopilot #AutonomousAgents #SoftwareEngineering #TechLeadership

GitHub's coding agent doesn't write code for you. It exposes whether your workflow deserves automation. Is your repository clean enough for background execution? Can your team define tasks precisely enough for an agent to act on them without constant correction? Most engineering teams answer "yes" instinctively. The agent will answer honestly. The real friction isn't adoption. It's that GitHub's own documentation lists explicit constraints: one pull request per task, repository-scoped execution, vulnerability to prompt injection, blockable by repository rules. That is not a limitation to work around. It is a mirror held up to your current process quality. The Invisible Tax pattern shows up here. Teams treat AI tooling as a patch for unclear ownership and weak review discipline. Because the agent inherits whatever mess exists in the repo, output quality degrades fast, and blame lands on the tool rather than the workflow. I've watched engineering leaders approve AI tooling budgets before auditing whether their task definitions are specific enough for a human to execute without a follow-up meeting, let alone an agent. - Repository hygiene determines agent reliability before any prompt is written - Review discipline must exist before background execution adds volume - Access controls and security considerations are non-negotiable, not post-launch tasks - AI accelerates a good workflow; it compounds a broken one The threshold most teams skip: what task-clarity standard must exist before agent-assisted work produces net positive output? That number varies, and few teams have defined it. The missing piece is ownership. Who is accountable when an agent-opened pull request introduces a regression nobody caught? A clean workflow beats a clever tool. Process quality trumps tooling ambition. Let's audit one repository your team would assign to an agent first, and assess honestly whether the task boundaries and review gates are ready for it. #AIStrategy #SoftwareEngineering #ProductLeadership by Dr. Hernani Costa, CEO & Founder of First AI Movers part of Core Ventures

🤖 How Claude Code completely changed the way I work I've been using Claude Code for a short while now, and honestly didn't expect it to have this much impact on my workflow. It's not just "AI that writes code" — it's a fully integrated development partner. ───────────────────── 🔄 The Workflow I've Built: ───────────────────── 1️⃣ Full GitHub Integration Claude Code opens a new branch from development (or any branch I choose) — I stay in control of the starting point. 2️⃣ Plan Mode — before a single line is written It gives me a complete breakdown of what it's going to do. I can review, adjust, and push back before anything gets implemented. This alone saves enormous time — you know exactly what's going to be generated. 3️⃣ Commits & Pull Requests It pushes commits to its own branch, then I open a PR against development. 4️⃣ GitHub Copilot does a proper Code Review Copilot reviews the PR and leaves detailed, actionable comments. 5️⃣ I add my own comments on top of Copilot's Then I go back to Claude Code — it reads both Copilot's feedback and my personal notes together and applies the changes. ───────────────────── The result? High-quality output, near-zero errors, and my job has essentially become: orchestrating ideas and using modern tools the right way. The part most people overlook 👇 The fixed System Prompt. When you give Claude clear, consistent instructions about how you work — the results shift dramatically. Some of mine: • Never generate or run migrations — I do those manually • Never push to development or production • Read the existing codebase first before writing anything • Always ask me before making decisions AI isn't a replacement for thinking — it's a multiplier for thinking correctly. 🚀 Do you have a different workflow or something that's worked well for you? Share it in the comments! 👇 #ClaudeCode #AI #SoftwareDevelopment #DeveloperTools #GitHub #Productivity #AIEngineering