Automated Development Workflow Improvements

WHY "SHIFT LEFT" IS THE NEW SDLC STANDARD 🚀 In my 14 years in Enterprise Security, I’ve seen how one "small" logic gap in a requirements can lead to weeks of rework and critical production bugs. By leveraging an AI stack of Claude (for logic and functional builds) along with Figma and Lovable (for UX and interactive prototyping), I’m moving "Quality" to the very start of the Product Discovery phase. Instead of waiting for a dev build or staging environment, I’m building interactive prototypes to: 🔹 VALIDATE THE USER FLOW: If I can find a navigation bottleneck in a clickable prototype, I’ve just saved the engineering team a massive refactoring effort. 🔹 VERIFY REQUIREMENTS: Stakeholders can "test drive" the app early. This eliminates the "I thought you meant X" conversations during UAT. 🔹 VISUAL AND FUNCTIONAL REFERENCE: For Devs and QA, having an interactive source of truth is worth 100 pages of static documentation. It clarifies edge cases before a single line of production code is written. AI isn't just for writing code—it’s for ensuring we build the RIGHT PRODUCT, the RIGHT WAY, the FIRST TIME. #ShiftLeft #Productmanagement #SDET #SDLC #SoftwareProcess #QualityAssurance #AI #ClaudeAI #Lovable #AgileDevelopment

 Is the SDLC dead? Long live the ASDLC. For decades, the Software Development Life Cycle (SDLC) has been our North Star. It’s linear, predictable, and—let’s be honest—often the bottleneck. We move from Planning to Design, then to a long stretch of manual Coding, Testing, and finally Deployment. But we are entering the era of Agentic Software Development. The difference isn't just "using AI to write code." It’s about moving from a Chain to a Loop. Traditional SDLC: The Human Relay Race  * Linear: One stage must finish before the next begins.  * Human-Centric: Humans are the "routers" of information between Jira, IDEs, and GitHub.  * High Latency: Testing and bug fixing happen after the heavy lifting is done. Agentic SDLC (ASDLC): The Autonomous Engine  * Iterative & Agentic: AI agents don't just suggest code; they research the docs, write the feature, run the tests, and fix their own errors before a human even sees a PR.  * Orchestrated: Humans shift from "Casters" to "Architects." We define the intent; the agents manage the execution.  * Self-Healing: The cycle includes a continuous feedback loop where agents monitor logs and deploy patches autonomously. The bottom line: In the traditional SDLC, the developer is the engine. In the Agentic SDLC, the developer is the pilot. The goal isn't to replace the engineer; it's to eliminate the "toil" that fills 60% of our day, allowing us to focus on high-level system design and solving actual business problems. Are you already integrating agents into your workflow, or are you still running the relay race? Let’s discuss in the comments. #SoftwareEngineering #AI #AgenticWorkflows #LLMs #SDLC #FutureOfCoding #DevOps

 If someone is only focus on SDLC, one is just a factory. There is a massive difference between delivering software and delivering value. Many teams treat the Software Development Life Cycle (SDLC) as their bible. Requirements -> Design -> Code -> Test -> Deploy. It’s reliable. It’s safe. But it often misses the "messy" front end. That’s where the Application Development Life Cycle (ADLC) comes in. ADLC asks the hard questions before a single line of code is written: 1. Discovery: Does the user actually need this? 2. Definition: What does success look like (beyond "it works")? 3. Design: Is the experience intuitive? 4. Feedback: Are we solving pain or just adding features? The Hierarchy: Think of ADLC as the strategy (the map) and SDLC as the tactics (the vehicle). You need both to reach the destination. #SoftwareDevelopment #TechDebt #ProductDiscovery #Agile #Coding #Leadership

Whole industry and community is trying to make SDLC into ADLC. SDLC - Software development Lifecycle ADLC - Agentic Development Lifecycle When one has to change the process to ADLC, one has to look into multiple points. Step 1 1. Choose which tool (Github copilot, Cursor, Claude code) to use strategically. 2. Choice of model 3. Integrating with external systems for making the process autonomous 4. Observability, tracing 5. Security - When tooling goes online to search and help in agentic development, it can connect with malicious tools also. So its very important to look at security and guard rails. Step 2. 1. How to use tools effectively by optimising on tokens 2. Applying use cases effectively 3. Model choice also as per use case. 4. External connectivity Step 3 is making the process more autonomous by working on agentic approach and working on long running tasks 1. Making the development Agentic 2. Creating tools on topic of copilots that help in modernization or migration that needs long running tasks Step 1 is where Organization senior leadership takes a decision Step 2 is where leads take the ownership to define the process Step 3 is where senior engineers take the process forward to make it Agentic or AI native I hope it helps. Share ur thoughts and define ur framework. #ADLC #SDLC #Agenticai #ainativeengineer #aidevelopment #agenticdevelopment #aileadership #aicoe #aiframework

We’ve spent decades trying to perfect requirements. And still got them wrong. AI is not just making development faster—it’s forcing us to rethink how we build software altogether. This piece explores a shift I’ve been thinking about: From deterministic requirements → to hypothesis-driven delivery From SDLC → to an Agentic Delivery Lifecycle (ADLC) From “getting it right upfront” → to “learning fast with real users” The key insight: Speed lives at the edge. Trust lives at the core. Curious how others are approaching this balance. #ADLC #AgenticTeam #SDLC

🛑 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗱𝗼𝗻'𝘁 𝗳𝗮𝗶𝗹 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗶𝗹 𝗮𝗿𝗼𝘂𝗻𝗱 𝗶𝘁. Hand a Jira ticket to an AI copilot and ask for a merged PR. Watch it fall apart. That gap is exactly what AISDLC solves. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝗜𝗦𝗗𝗟𝗖? 🧠 A multi-persona agent skill that takes any feature input - ticket, PRD, Figma, or text and drives it through the SDLC to a pull request. Not single-shot generation. A structured pipeline with 5 AI personas. 𝗧𝗵𝗲 𝗙𝗶𝘃𝗲 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝘀 🏗️ → 𝗣𝗠: Produces a Work Spec with Given/When/Then criteria. Nothing gets built without it. → 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁: Decomposes work and reviews diffs. Verdicts are APPROVED or CHANGES REQUIRED. → 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: The only one writing code. Runs a 17-point self-review. Never ships a TODO. → 𝗤𝗔: Verifies tests exist (hard gate), runs the suite, and checks AC. → 𝗨𝗫: Runs last on frontend work for visual fidelity & accessibility. Every persona can block progress. 𝗪𝗵𝘆 𝗶𝘁 𝗯𝗲𝗮𝘁𝘀 𝘀𝗶𝗻𝗴𝗹𝗲-𝘀𝗵𝗼𝘁 𝗔𝗜 ⚡ → 𝗛𝘂𝗺𝗮𝗻 𝗴𝗮𝘁𝗲𝘀: Decomposition is human-approved before any code is written. Bad spec = bad code. → 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀: Architect rejects, QA finds bugs. The pipeline loops until done. → 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝘀𝘁𝗮𝘁𝗲: Sessions survive context limits. Resume later exactly where you left off. → 𝟭𝟯 𝗵𝗮𝗿𝗱 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: No skipping reviews, no pushing without approval. 𝗧𝗵𝗿𝗲𝗲 𝗔𝘂𝘁𝗼-𝗥𝗼𝘂𝘁𝗲𝗱 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 🎯 ① 𝗙𝘂𝗹𝗹: Multi-file features with holistic architecture review. ② 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱: Typical stories. ③ 𝗙𝗮𝘀𝘁: Hotfixes & bugs. 💡 𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Most tools optimize for appearing done. AISDLC is built for the path from input to merged PR. Full breakdown including the pipeline diagrams, input adapters, and state machine - linked in the comments. What part of your SDLC do you think AI agents will take over first? 👇 #AgenticEngineering #AISDLC

Everyone's saying the SDLC is dead. That AI collapses it into one fast loop. I don't think it collapsed. I think it split into two. Loop 1: figure out what to build. Rapid prototyping, quick iterations. AI makes this absurdly fast. Test three approaches before lunch, throw away what doesn't work. Messy and cheap to fail. Loop 2: build it for real. Take the clarity from Loop 1, write a structured spec, hand it to a coding agent. The agent follows it precisely. No improvising. I tried blending both at once. Scope creep, silent bugs, specs that drifted from reality. The freedom that makes prototyping powerful is exactly what makes production fragile. So I separated them. The spec is a markdown file that lives in the repo alongside the code. No special tools. Agents can read and traverse MD natively. The spec drives every agent interaction. When something breaks, you check the spec first. And agents don't modify the intent or architecture layers. If those need to change, a human reviews that change. The spec is protected where it matters. This is still early. I don't know how it scales beyond a handful of developers. But what I'm seeing so far: separating these loops produces cleaner code than blending them.

Legacy SDLC is dying. I am a fan of what Aakash Gupta writes regarding Product and a big fan of his posting on this subject. I have been approached by many companies trying to sell me on “agentifying” the SDLC but their vision is same old legacy process just with agentic capabilities integrated in the seams. I think there is a market for that now and acknowledge that the Claude team is a bit of an anomaly but the IMO the reality is this will be the new norm in a year at most. Every company should be incubating these new models and redefining their way of working. https://lnkd.in/gfmvB8NM

In a 2-hour sprint, there is zero room for "maybe." When you work in 120-minute bursts, ambiguity is the enemy. We cannot have tasks that take hours. We need tasks that take minutes. If a requirement is vague, an AI agent would fill that gap with a hallucination, and the sprint is wasted. At Vetta, we solved this by using an Agentic Product Owner. This is the high-speed translator between my strategy and the team’s execution. It takes my vision as a Product Manager and deconstructs it into atomic tasks. These are small, precise instructions that a human can validate in a single glance. We keep a human in the loop at every step: The Planner: The agent takes my strategy and deconstructs it into atomic tasks. It automatically generates the GitHub issues and provides the specific instructions each human-AI pair needs. The Traffic Controller: During our 60-minute bursts, it watches the file boundaries. If two developers are about to hit a conflict, the agent catches it early. No more "merge hell" on Friday afternoons. Human-Driven Quality: Each atomic task comes with Acceptance Criteria that require human review and approval. Automated testing is built in, so humans do not waste time on invalidated code. I am managing intent. I focus on strategy and customer validation. My agentic P.O. handles the administrative overhead: generating tasks, creating GitHub issues, and providing instructions for other agents. This is agile development at the speed of now.