Claudio Bisanti’s Post

Stop calling yourself a "coder." If your primary professional identity is tied to manually typing out syntax, your mental model is obsolete for 2026. The tectonic shift we’ve been anticipating is here. We are no longer authors of code. We are software developers and architects designing environments where agents write the code. If you are leading an engineering team or building scalable systems today, here is why your roadmap needs an immediate reality check: Stop Chasing the "God Model" OpenAI’s Codex team recently pushed 1 million lines of production code with zero human-written lines. This was orchestrated by just three engineers using a sophisticated "harness." Drop a baseline model into a weak environment, and it scores 42% on coding benchmarks. Wrap that exact same model in a strict harness—hard constraints, aggressive linters, autonomous test execution—and it jumps to 78%. The breakthrough isn't a smarter AI. It’s the scaffolding. From Conductor to Orchestrator Up until now, AI development has felt like pair programming (acting as a conductor). To scale, you must become an orchestrator. You are no longer guiding a single instrument; you are defining system architecture and deploying parallel agents to tackle independent nodes simultaneously. The 80/20 Reality of Autopilot Anthropic's 2026 data shows developers use AI in ~60% of their daily workflows, but fully delegate only 0-20% of tasks. The remaining 80%? Active collaboration. AI handles syntax; human systems thinking prevents cascading architectural collapse. The 2026 Architect’s Playbook: 1. Specs are Production Code: Vague prompts generate technical debt. Precise, edge-case-aware specs are now your highest-leverage skill. 2. Automate the Feedback Loop: Agents must be able to compile code, run tests, and iterate on their own failures before you review a PR. 3. Fortify the Boundaries: Catch algorithmic drift at the perimeter with strict architectural rules and "taste invariants." Over the past year, "vibe coding" captured the industry's imagination. But vibe coding without a robust engineering harness is like driving a supercar at 150 mph without a steering wheel. The engineering rigor didn't disappear—it just moved one layer up the stack. Are you focusing your team's cycles on the models, or the harness? Let’s discuss. 👇 #SoftwareEngineering #SystemsArchitecture #AIAgents #TechLeadership #FutureOfWork

Most teams are still building software like this: Developers → Code → Testing → Fixing bugs But that model is starting to break. AI is not just accelerating development. It’s changing how engineering systems are designed. Modern teams are moving toward something very different: Architecture → AI-augmented development → Automated validation → Continuous optimization The shift is not about coding faster. It’s about building systems that improve themselves. Which model is your team closer to today? #SoftwareEngineering #ArtificialIntelligence #SystemDesign #TechLeadership #AI

We’re seeing a clear shift in how software is built. Most teams are still focused on writing code faster. But the real change is happening at the system level — how architecture, workflows, and validation are designed. AI is not just accelerating development. It’s reshaping how engineering systems work. This simple diagram captures that shift 👇

Over 80% of developers use AI, but how many are actually seeing consistent productivity gains? 🤔 While AI can speed up delivery by 30-50%, it can just as easily add overhead through unclear logic, rework, and endless validation. The difference between a massive speed boost and a massive headache lies in your workflow, not just the tools. In our new article at Devessence, we break down what actually works (and what doesn’t) in AI-assisted software development. Key highlights: 🔹 Why specification-driven development (SDD) beats direct prompting. 🔹 The dangers of generating large code batches at once. 🔹 How the developer's role is shifting from writing code to reviewing and validating. 🔹 Proven strategies to keep control over code quality and system behavior. Ready to build a sustainable AI productivity strategy and stop the guesswork? 📖 Read the full breakdown here: https://lnkd.in/dcbvhbg2 #SoftwareDevelopment #ArtificialIntelligence #TechTrends #SoftwareEngineering #Devessence

🚀 30,000+ lines of code in a single day. Today, I paired with an AI agent to tackle a massive project evaluation, and the output velocity was unreal. The feeling? An absolutely unstoppable flow state. ⚡️ Integrating an AI agent didn't just accelerate the build it fundamentally shifted my role from writing syntax to directing architecture and logic. The developer experience (DX) was incredibly enjoyable. Here is why this workflow is a game changer: - Automated Scaffolding: Boilerplate and repetitive structures are handled instantly, allowing me to focus entirely on core business logic. - High-Velocity Evaluation: I was able to spin up, iterate, and evaluate complex project modules with practically zero latency. - Uninterrupted Flow: Minor debugging and syntax bottlenecks essentially vanished, keeping momentum at an all-time high. The paradigm of software engineering is shifting from manual execution to high-level orchestration. #SoftwareEngineering #AI #DevEx #Architecture #Productivity #TechInnovation

Most engineers try to understand AI agents by reading code. That’s the wrong abstraction. In agent systems, complexity doesn’t live in functions or classes. It lives in decision paths. Why did this go to “help” instead of “answer”? Why did it fallback? Why was the response skipped entirely? You can trace 5 files to answer that. Or you can look at a single line in a decision map. I started using something I call a Decision Map: input condition → branch → outcome Example: mode != "answer" → immediate_reply_skip → skip response supportTier == "reject" → out_of_scope → fallback That’s it. No implementation details. No abstraction layers. Just behavior. What changed: Debugging became trivial Instead of tracing code, I trace decisions. Collaboration improved Non-engineers can understand agent behavior without reading code. Testing became clearer Coverage is no longer “functions tested” → it’s “decision branches covered” Safer iteration I can modify one branch without guessing system-wide impact. Agents are not just software systems. They are decision systems. And once you model them that way, you stop reading code to understand behavior. You start reading decisions.

The shift in software development isn’t just about AI writing code. It’s about who, or what, is doing the work. We’re starting to see early versions of multi-agent systems, where different agents take on roles across architecture, development, testing, security, and operations, all running in parallel. That changes the shape of engineering. Work stops being linear. It becomes orchestrated. The bottleneck moves away from code production and toward coordination, making sure outputs stay consistent, constraints are respected, and systems don’t drift. Because once multiple agents are operating at the same time, small inconsistencies compound quickly. So the role of the developer shifts. Less time writing code. More time designing the system those agents operate within, defining boundaries, enforcing structure, and making autonomy safe to use. That’s the part that’s still missing in most conversations. #AgenticAI #SoftwareSystems #AI

AI writes code faster now. No doubt. But "replace engineers"? I don’t buy it. AI can generate blocks of code in seconds, sure. But who’s going to: - Understand why that code exists in the first place? - Fix the logic when it breaks in production? - Ask the critical questions before a single line is even written? Tools change. Builders don’t. As engineers, our value isn’t just in our typing speed, it’s in our ability to solve problems and architect systems that actually scale. What do you think? #software #architect #systemdesign #vibecoding #aicoding

🚀 12 Claude Code Features Every Engineer Should Know In today’s AI-driven development world, tools like Claude are not just assistants — they’re becoming true engineering partners. Here are some powerful features that can seriously level up your productivity 👇 ✨ Highlights: • 📄 CLAUDE.md → Your project’s memory brain • 🔐 Permissions → Control what AI can access • 🧠 Plan Mode → Review before execution (game changer!) • ⏪ Checkpoints → Undo mistakes like time travel • 🧩 Skills & Plugins → Reusable intelligence + integrations • 🔗 MCP → Connect APIs, DBs, external systems • 📦 Context Management → Feed the right data at the right time • ⚡ Slash Commands → Automate workflows instantly • 🧵 Compaction → Optimize tokens smartly • 🤖 Subagents → Parallel problem solving (next-level productivity) 💡 Why this matters? We are moving from coding manually → orchestrating intelligent systems. The engineers who understand these concepts early will lead the next wave of innovation. 🔥 My takeaway: AI won’t replace developers — but developers who leverage AI will replace those who don’t. What feature excites you the most? 👇 #AI #SoftwareEngineering #ClaudeAI #Productivity #Developers #TechLeadership #SystemDesign #Innovation image credit: ByteByteGo

𝗔𝗜 𝘀𝗮𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘁𝗶𝗺𝗲 𝗶𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗿𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀) A lot of conversations around AI in engineering are still too generic -- "Use AI to code faster." or "Use AI to improve productivity." Sounds good. But where does it actually save time in real software teams? From what I'm seeing while working with customers, the biggest gains are usually not in writing core business logic. They come from the repetitive, time-consuming work around it. Here are some areas where AI is creating real leverage: 1. Boilerplate & Scaffolding -- Setting up APIs, DTOs, models, CRUD layers, test skeletons, config files. This work is necessary, but it rarely needs deep human creativity. AI can speed this up significantly. 2. Refactoring Assistance Cleaning old code, improving readability, suggesting modularization, converting patterns. Still needs review -- but it can save hours of manual effort. 3. Documentation API docs, README files, technical summaries, onboarding notes, release notes. One of the most ignored areas in engineering and one of the easiest wins with AI. 4. Debugging Support Explaining stack traces, identifying possible root causes, suggesting checks. Not a replacement for debugging skills -- but a strong accelerator. 5. Test Case Generation Unit test scenarios, edge case ideas, negative flows, regression coverage suggestions. This helps teams improve quality faster. 6. Repetitive Operational Tasks SQL query drafts, log parsing scripts, data transforms, migration helpers, internal automation. High ROI use case. But here's the important part. 𝗔𝗜 𝘀𝗮𝘃𝗲𝘀 𝘁𝗶𝗺𝗲 𝗼𝗻𝗹𝘆 𝘄𝗵𝗲𝗻 𝘂𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗹𝗲𝗮𝗿 𝗿𝗲𝘃𝗶𝗲𝘄 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀, 𝗰𝗼𝗱𝗶𝗻𝗴 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀, 𝗮𝗻𝗱 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝘆𝗼𝘂 𝗺𝗮𝘆 𝗴𝗮𝗶𝗻 𝘀𝗽𝗲𝗲𝗱 𝘁𝗼𝗱𝗮𝘆 𝗮𝗻𝗱 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗲𝗰𝗵 𝗱𝗲𝗯𝘁 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄. In my experience, the best teams use AI for:  • Low-value repetitive effort  • Faster first drafts  • Better engineering velocity And they reserve human thinking for:  • Architecture  • Trade-offs  • Customer problems  • Long-term maintainability That’s the real model. Use AI where it creates leverage. #AIinEngineering #CTOInsights #TechLeadership #EngineeringLeadership #GenAI #SoftwareEngineering #ProductEngineering #DeveloperProductivity #EngineeringCulture #DigitalTransformation #ScalableSystems #Innovation #FutureOfWork #Leadership