Vanchinathan Ayipalayam Chandrasekaran

Evolution of Requirement Specification into Prompts as Executable Specs In the history of software engineering, few artifacts have been debated, maligned, celebrated, and reinvented as persistently as the requirements document. What started as a formal engineering contract in the 1970s has transformed through decades of methodology shifts, cultural changes, and now — most dramatically — the rise of artificial intelligence. The question today is no longer just "what do we need to build?" but "how do we express that intent so clearly that both humans and machines can act on it?" "The history of requirements is the history of how well teams could communicate intent across time, roles, and now — machines." From the rigid specification documents of the waterfall era, through the lightweight user stories of Agile, to the emerging discipline of AI-native spec-driven development where the quality of your written specification directly determines the quality of the code that gets produced. As agentic AI systems become more capable — able to execute multi-step engineering tasks, coordinate across codebases, and make implementation decisions autonomously — the role of the requirements writer is not diminishing. It is becoming more important and more consequential. The best practitioners of requirements writing in the coming decade will be those who can operate at the intersection of customer insight, systems thinking, and AI fluency. They will understand not just what to ask for, but how to express it in forms that unlock the full power of AI-assisted development while maintaining the human judgment, ethical accountability, and strategic vision that no machine can provide. The waterfall document was a monument to completeness. The Agile story was a monument to conversation. The AI-native spec is something new: a bridge between human intent and machine execution, written with the care of a legal document, the empathy of a user researcher, and the precision of an API contract. "The best requirement you will ever write is the one so clear that it needs no follow-up questions — from a human or a machine." That bar has always been worth striving for. Now, for the first time, we have systems that will immediately reveal whether we met it.

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Payments have always worked on a simple idea: A person decides to buy something. That is what the whole system is built around. Now that is starting to change. With things like Visa’s “Agentic Ready” work, we are moving toward a world where software does not just help you decide… It can actually make the purchase. That is a pretty big shift. Because payments were never just about moving money. They are about knowing: 1. Who made the decision 2. Why it was made 3. Who is responsible When a person clicks “buy,” that is clear. When an AI agent does it, it is not so obvious. Who owns that decision? How do you know it was authorized correctly? What happens if something goes wrong? This is not just a new feature. It changes how the system needs to think. Instead of: Person → Payment It becomes: Person → Rules → AI → Payment And suddenly, trust looks different. We are no longer just verifying people. We are starting to verify software acting on their behalf. That is going to be interesting to watch. Ref: https://lnkd.in/gajP2gab

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Why Product and Technology Are Merging — and Why New Roles Are Emerging/Emerged The lines between product and technology are blurring faster than ever. Roles like CPTO (Chief Product & Technology Officer), Product Solution Developers, and Technical Project Managers are in high demand. Why? Because success today requires leaders and builders who can: Think beyond just tech or just product. Balance business value with technical feasibility. Deliver a Minimum Viable Product (MVP) that’s quick, scrappy, and fast to market. Then, once validated, scale with resilience, throughput, and availability. 🚀 In today’s world, speed matters — but so does sustainability. The winning teams are those that validate ideas early, fail fast, learn, and then double down on the right bets with solid engineering foundations. As technology leaders and product owners, we must embrace this hybrid mindset: blending business insight with technical depth. That’s where the future of product innovation lies. 👉 Do you see product and technology continuing to merge, or do you think these roles should stay distinct? #ProductManagement #TechnologyLeadership #Innovation #MVP #AI #Scale

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PART 1 — From Feature PM to System PM The day I stopped asking "what should we build next?" and started asking "how should this system behave?" — everything shifted. It wasn't subtle. It felt like switching from checkers to chess. Here's what changed: ───────────────────────── Feature PMs operate in a linear world: Problem → Solution → Ship → Measure That loop works when the product is static. When features add up to a coherent experience. AI breaks that loop. Because AI products don't have fixed behavior. They have behavior that EMERGES FROM A SYSTEM — and that system changes as it sees more data, different users, changing contexts. The old unit of PM work was the feature. The new unit is the DECISION LOOP. ───────────────────────── The decision loop has four parts: 1 — INPUT DESIGN: What data does the system see? 2 — MODEL BEHAVIOR: What does it decide? 3 — OUTPUT DESIGN: What actions does it take? 4 — FEEDBACK DESIGN: What does it learn from the result? A Feature PM thinks about #2 and #3. A System PM thinks about all four — and the connections between them. ───────────────────────── The analogy that clicked for me: A vending machine has features — buttons, slots, outputs. Every interaction is identical. It doesn't get better from use. A restaurant is a system — front of house, kitchen, supply chain, live feedback from every table. It learns. It adapts. It compounds. Feature PMs build vending machines. System PMs build restaurants. ───────────────────────── What this looks like in practice: FEATURE PM asks: "Should the AI suggest this automatically, or wait for the user to ask?" SYSTEM PM asks: "What signal tells us the suggestion was useful? How does the system improve from that signal over time? What happens if the signal is noisy?" Same feature. Completely different scope of thinking. ───────────────────────── Two real examples: SPOTIFY didn't just build a recommendation feature. They built a system where every play, skip, save, and share becomes a signal that refines future recommendations. The feature is the surface. The system is the moat. CHEGG added AI features as a reaction to ChatGPT — without rearchitecting the value they delivered. They responded to the surface, not the underlying shift. ───────────────────────── The shift isn't that features don't matter anymore. It's that features only create lasting value when they're designed as part of a system. The AI PM's job isn't to ship features into a system. It's to design the system that the features live inside. This is Part 1 of a 6-part series: How AI PMs Design Systems That Actually Win Part 2 drops next → AI Stack Decisions You Can't Delegate Part 0: https://lnkd.in/gYeWzXZv Follow & save this if you're a PM building on AI. #ProductManagement #AIPM #TechnicalPM #AIProductManagement #ProductLeadership #ProductStrategy #PrincipalPM #SystemDesign #ProductThinking #AIProducts

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If agentic systems can generate code (or content, workflows, configs) at near-zero marginal cost, then “more output” cannot be the north star. The stock(https://lnkd.in/gXYbUxTr) will explode. The real design question becomes: how do we build agentic systems where stock health — not inflow velocity — is the primary objective? Here’s a simple framework. First, separate Flow Optimization from Stock Optimization. Most agents today are optimized for flow: number of PRs, tasks closed, tokens generated, features shipped. A stock-health system explicitly measures the condition of the accumulated asset — cohesion, complexity, defect density, infra cost, latency, security exposure, change failure rate. If it’s code, track architectural entropy and long-term maintainability. If it’s data, track lineage integrity and semantic consistency. What gets measured becomes the system’s gravity. Second, embed a Reflexive Feedback Loop. Every act of generation must create a shadow of evaluation. Agents shouldn’t just write — they should simulate impact: dependency ripple, test coverage delta, performance implications, cost drift. In systems terms, generation must automatically trigger balancing signals. If complexity crosses a threshold, refactoring tasks are auto-created. If coupling increases, modularization is incentivized. The agent is rewarded not just for adding, but for simplifying. Third, align incentives around Net Stock Quality, not Gross Output. Reward deletion. Reward consolidation. Reward reduction in cognitive load. An agent that removes 1,000 lines of unnecessary code should score higher than one that adds 10,000. This flips the reinforcing loop. Instead of “more code → more work → more code,” you get “more complexity → more pruning → restored equilibrium.” Finally, introduce a Steward Layer — human or meta-agent — whose sole mandate is long-horizon coherence. Not velocity. Not tickets closed. But architectural narrative. This layer ensures that local optimizations by agents don’t fragment the system over time. Think of it as system memory and taste. In short: build agents that generate, evaluate, prune, and remember. When stock health becomes the objective function, reinforcement and balancing loops coexist intentionally — and abundance becomes leverage instead of entropy. #systemsthinking #ai #agenticai #systems

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Modern PMs need to build. Not because you'll become an engineer. Because it's the fastest way to: → Understand technical debt → Spot integration risks early → Get a little more technical insight → Make informed tradeoffs I can't write Swift from memory. But I built HomeTend in 3 months using AI tools. That's enough to change how I manage technical programs. The barrier isn't skill anymore. It's willingness. What's stopping you from building something? #ProductManagement #TechnicalLeadership #BuildInPublic

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Classic "chunk-and-stuff" RAG forced a tradeoff: Trust OR Speed. Enterprises demand Trust  → citations, permissions, compliance, audit trails etc. Consumers want Speed  → fast, clean, direct answers "from anywhere" without friction and great UX. But what about Trust AND Speed? Graph-augmented → enterprise trust through structured relationships and complex ACLs. Memory-augmented → user speed by persisting knowledge. Agentic retrieval → multi-step reasoning for accuracy and efficiency. Hybrid fine-tune + RAG → enterprise-sensitive data fine-tuned, long-tail consumer data retrieved. Generative indexes → pre-built knowledge layers for faster, more grounded responses. The future is Hybrid RAG: Source-aware under the hood → trust is never *ever* compromised. Adaptive presentation → speed for user. Together → trusted AND fast.

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🚀 Just watched an insightful YouTube video on Requirements and Testing that breaks down the essentials of building robust software! 🎥 If you’re looking to sharpen your skills in defining clear requirements and implementing effective testing strategies, this video is a must-watch. It dives into best practices, practical tips, and how to ensure quality at every stage of development. As a developer, I’m going to close this as not a bug since the user isn’t using it as per the requirements . It should be an enhancement request.:-) Check it out here: https://lnkd.in/gMtaunJ4 #SoftwareDevelopment #Testing #RequirementsEngineering #TechTips

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The recent Claude for Small Business announcement is literally earth shattering. Is it now possible to bring "all-in-one" functionality into one user experience by stitching best of breed providers together, as long as those providers have a decent API suite. This will be great for giving customer choices. A new breed of middleware, and an agent of agent layer is now needed to do seamless stitching, from universal profiles & boarding to transparent payment integrations and simple billing interfaces. This will force "all in one" players to make each core module of their suite better to compete on their own merits, and obviously now invest in headless mcp wrapped APIs full force to stay relevant. Beyond SMB, many more Claude Work like opportunities in SaaS verticals where traditionally workflow integrations were difficult between CRM/small ERPs system and other 3rd parties. Great times for builders.

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🎯 Pricing Isn’t Just a Number—It’s a Strategy. In today’s competitive lending landscape, the ability to offer the right price to the right borrower at the right time can be the difference between winning or losing a high-quality applicant. According to the Accenture Global Banking Consumer Study 2025: ✅ Banks in the top 20% for customer advocacy saw 1.7x faster revenue growth than their peers—and up to 2.6x in North America. ✅ Customers expect pricing that reflects their behavior, their moment, and their relationship with the bank. ✅ Advocates—those who feel understood—hold 17% more products with their primary bank. ✅ Despite digital transformation, many banks remain “functionally correct but emotionally devoid.”  Personalized pricing helps rebuild that emotional connection. 💡 That’s where Symphonix Rate Cards come in. Our dynamic, rules-driven pricing engine empowers lenders to: 🔹 Tailor pricing based on borrower attributes 🔹 Adjust rates in real time by product, channel, or risk tier 🔹 Ensure compliance with version-controlled, auditable pricing structures 📈 Pricing isn’t just math—it’s momentum. Let Symphonix help you turn pricing into your competitive edge. #LendingInnovation #DigitalBanking #LoanOrigination #Symphonix #PricingStrategy #CustomerExperience #Fintech #BankingTransformation #DigitalLending20

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Almost anything can be built now. 🚀 That’s no longer the hard part. AI has collapsed the cost of execution. Prototypes in hours. Features in days. Shipping feels easy — sometimes deceptively so. What hasn’t changed: • Customers still ignore features 👀 • Attention is still scarce ⏳ • Adoption is still hard 📉 At scale, the risk isn’t slow delivery — it’s mistaking output for impact. I see teams add AI features because they’re easy to build, not because they change a meaningful user decision. Roadmaps expand. Velocity looks good. Outcomes don’t. When buildability explodes, judgment becomes the bottleneck ⚖️ This is where the TPM leverage lives: • Shaping bets, not just plans 🎯 • Forcing clarity on outcomes before teams commit • Applying restraint when marginal value is low ✋ Execution still matters. But in a world where anything can be built, deciding what not to build is the leadership skill. The hardest question isn’t “Can we ship this?” It’s “What happens if this ships?” 🤔

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I have met far too many talented young individuals from underprivileged backgrounds who had the ability… but not the access. The intelligence… but not the exposure. The hunger to grow… but not the opportunity. Talent should never be limited by circumstance. At Xcelevate, our sole mission is to create opportunities for underprivileged youth who are ready to rise but lack the platform to do so. We are not just training students. We are unlocking potential. We are building confidence. We are opening doors that once felt permanently closed. And when one young person transforms, it doesn’t stop there. Families change. Communities evolve. Future generations begin to think differently. That is how multi-generational impact is created. And that is why Xcelevate Skills Foundation exists.

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Google's Head of Product Madhu Gurumurthy, says they’re shifting from a writing-first to a building-first culture... about time. I see this as a smart move. When a PM can vibe-code a demo in the time it takes to write a PRD, you stop debating paragraphs and start reviewing reality My take: If something brings more value than a PRD, move forward with the prototype and document only what the demo can’t show In my experience, working with platforms with more wider features, can be trickier than pure user-facing flows, but you can still "prototype the invisible" like flows, behaviors, and expected results I recommend this great article https://lnkd.in/e7Rxsuba (A guide to AI prototyping for product managers)

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Slash is the autonomous agent platform we built inside Razorpay. It writes code, reviews PRs, debugs incidents, and answers questions across teams. A quarter in, the way we build product has shifted. Non-engineers raise their own PRs. Account managers debug stuck payments without pinging engineering. ~1,000 PRs a week ship through Slash. 1 in 3 merge with zero human review. The shift isn't about replacing engineers. It's about what they, and everyone else, get to spend their time on now. Just wrote about how we got here: https://lnkd.in/gf95aaR4

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The "Chai Tapri" & API: Socho ye situation: You go to a Chai Tapri and ask for a "Cutting." You don’t go inside the kitchen, turn on the gas, or find the ginger. You talk to the waiter, and he brings the tea to you. In tech, that waiter is an API (Application Programming Interface). The kitchen is the "Server" where all the complex work happens. You are the "User." You don’t need to know how the stove works; you just need to know how to ask the waiter. When you use a travel site to book a flight, the site uses an API to "ask" the airline's kitchen if seats are available. The API brings the answer back to you. Simple language mein samjhein toh, APIs are the messengers that let two different apps talk to each other.

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The fear tax: why “we’ll add passkeys as an option” quietly keeps the riskiest path alive I keep hearing a familiar line from product leaders: “Our customers won’t learn passkeys. If we push this, we’ll lose users.” I get it. Nobody wants to be the person who “broke login” and triggered a wave of support tickets. But that instinct has a hidden cost. A fear tax. Because in security, “we’ll add it as an option” often means something else in practice: the weakest path stays alive forever. Most users don’t wake up excited to change how they sign in. If two paths work, they’ll stick with the one they already know. Not because they can’t learn, but because they’re busy. Familiarity is a feature. Attackers think differently. They don’t care that you offer a stronger method. They go after whatever is easiest, most reusable, most phishable, most socially engineerable. And if SMS or TOTP is still there, it becomes the path of least resistance. You can ship a shiny new phishing resistant method and still be anchored to yesterday’s weakest link. What’s tricky is that the concern often gets framed as “users won’t understand it.” I’m not sure that’s the real problem. People learn new patterns all the time when the upgrade feels worth it. They don’t learn when it’s presented like homework, hidden behind settings, and labeled “optional.” So the move isn’t just technical. It’s behavioral. Adding passkeys as an option can be a great start, as long as it’s not the finish line. If you want adoption, the stronger path has to feel better in ways users notice: fewer steps, less interruption, more confidence. Make it feel like an upgrade, not a lecture. Then comes the part teams avoid saying out loud. Keeping legacy methods forever isn’t neutral. It’s a decision. It’s deciding to leave the easiest door unlocked while hoping everyone uses the stronger one next to it. I’m not arguing for a reckless big bang migration. That’s a great way to lose trust. I’m arguing for direction. If passkeys are the destination, treat them like it. Earn adoption with a better experience, then retire the weakest links in a measured way. That’s what “customer friendly” looks like in security. Not protecting users from change, but protecting them from the weakest path you kept around because it felt safer to avoid the conversation.

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Incredibly proud moment. We started AgentCore payments at AWS with a mission of enabling AI agents to be fully autonomous yet secure when it comes to payments — and today it's live. 🚀 When we started this journey, the question was simple: if agents are going to do real work, they need to spend real money. No human in the loop clicking "confirm." No bespoke billing integrations for every endpoint. Just infrastructure-level payments that work at machine speed. Built in partnership with Stripe (Privy) and Coinbase, AgentCore payments lets agents autonomously discover, negotiate, and pay for services — APIs, MCP servers, web content, other agents — with stablecoin wallets governed by deterministic spending limits. This is the missing piece of the agentic stack: the economic layer. Agents that can reason, act, AND transact. Grateful to Matt Garman and AWS Agentic AI leaders (Swami Sivasubramanian, Preethi CN, David Richardson, Madhu Parthasarathy) for the vision and trust, and to the incredible engineering teams at Coinbase and Stripe (Privy) who made this real. Day one of a new era. 🚀 Special thanks to my awesome team for making it happen: Chethan Shriyan, Peter Jiang, Aanchal Arora, Raju Ansari, Khusbu Mishra, Shivank Goel, Tyler Devenere, Vikas Walunj and WenChuan Lee!

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🔁 Retries are not a recovery strategy When facing a failed request, retrying isn't a guaranteed fix. In fact, retries, if not handled correctly, can make the problem worse, because: ➡️ Retrying a failed payment could lead to double-charging the customer. ➡️ Retrying a failed write operation might result in state corruption. ➡️ Retrying a heavy database call could potentially bring down the entire database. ➡️ Retrying on a system already in a degraded state could lead to its failure. Retries without proper context simply amount to denial. Resilience goes beyond mere repetition, it involves understanding when to retry, how to do it effectively, and what steps to take if the retry fails. Here are some key considerations: ▪️Is the operation idempotent? ▪️Are retries targeted at specific error types? ▪️Have you implemented backoff mechanisms and jitter? ▪️Are timeouts correctly configured? ▪️What is the maximum load the system can handle before collapsing? ▪️Do you have a fallback plan in place? So, How do you currently manage retries in your architecture⁉️ What happens if all the retries fail⁉️ Are you ok with that⁉️ #DistributedSystems #SoftwareArchitecture #Resilience #Retries #BackendEngineering #DotNet #CSharp #AWS #Microservices #Kubernetes #Docker #SQL #CloudComputing #NET #SoftwareEngineer #TechLeadership #Kafka #RabbitMQ

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