Software Development Lifecycle In Engineering

"Agentic Code Review" - The hard part of engineering isn't writing code anymore. Coding agents are extraordinarily good now and getting better fast. But the hard part of engineering has moved from writing code to deciding whether to trust it. Code review is the big bottleneck. My latest free deep-dive: https://lnkd.in/gSZqtKDP ✍ AI pushes raw output up by about 4x, but real productivity gains sit closer to 12%. The gap between those numbers is review work. Because we poured machine-speed output into a system built for human-speed work, the friction has moved downstream: - PRs merged with zero human review are up 31.3% - Median review duration is up 441.5% - The per-developer defect rate has jumped from 9% to 54% How you solve this depends entirely on your blast radius. A solo developer vibe-coding a side project and a team keeping a ten-year-old enterprise system alive share almost no constraints. To adapt, the rules of code review have to change: Tier by risk, not author: Spend scarce human attention only where being wrong is costly. A config change gets a linter; a payments path gets the full stack of tests, multiple AI reviewers, and human ownership. Embrace heterogeneous AI review: CodeRabbit, Greptile, Seer, and others all catch different classes of bugs. Run at least two with deliberately different characters. Keep humans on the loop: The volume ended the era of a human reading every single line. Instead, humans must own the accountability, the high-stakes gates, and the judgment of whether the change was the right thing to build in the first place. We made writing cheap, but understanding a system well enough to stand behind it remains the most durable and interesting skill in software. I mapped out exactly where the work has shifted in my latest write-up and hope you find it helpful. #ai #programming #softwareengineering

Software development is quietly undergoing its biggest shift in decades. Not because of new frameworks. Not because of faster cloud. But because agents are entering the SDLC. Traditional development follows a slow, sequential loop: requirements → design → coding → testing → reviews → deployment → monitoring → feedback. Each step depends on human handoffs, manual fixes, delayed feedback, and long iteration cycles—often stretching from weeks to months. Agentic coding changes this entirely. Instead of humans writing everything line-by-line, developers express intent. Agents understand requirements, implement features, generate tests and documentation, deploy changes, monitor production, and even propose fixes. The lifecycle compresses from weeks and months into hours or days. Here’s what actually changes: • Sequential handoffs become continuous agent-driven flows • Humans shift from coding to guiding and reviewing • Documentation is generated inline, not after delivery • Testing happens automatically alongside implementation • Incidents trigger agent-assisted remediation • Monitoring feeds directly back into learning loops • Iteration becomes constant, not episodic In the Agentic SDLC: You describe outcomes. Agents execute workflows. Humans validate critical decisions. Systems learn continuously. The result isn’t just faster delivery. It’s a fundamentally different operating model for engineering—where feedback is immediate, fixes are automated, and improvement never stops. This is how software teams move from manual development pipelines to self-improving delivery systems.

𝟭. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 (𝗕𝗥𝗗): A BRD captures high-level business needs and objectives from a stakeholder’s perspective. It focuses on why a project is being undertaken and what value it brings to the business. 𝗞𝗲𝘆 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀: • Business objectives • Stakeholder needs • High-level business requirements • Scope of the project • Business rules • Assumptions and constraints 𝟮. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 (𝗙𝗥𝗗): An FRD translates high-level business needs into detailed functional requirements that describe how a system should behave. It focuses on system interactions, workflows, and features that will fulfill business requirements. 𝗞𝗲𝘆 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀: • Functional requirements (detailed descriptions of features) • System workflows • Use cases and user stories • UI/UX requirements (screens, wireframes) • Data flow diagrams 𝟯. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗦𝗥𝗦): An SRS is a comprehensive document that includes both functional and non-functional requirements, providing a complete specification of how the software should work. It is often used by developers and testers for system implementation. 𝗞𝗲𝘆 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀: • Functional requirements (features & capabilities) • Non-functional requirements (performance, security, scalability) • System architecture & design constraints • Data models • Interfaces (API, external system interactions) While the 𝗕𝗥𝗗, 𝗙𝗥𝗗, and 𝗦𝗥𝗦 serve different purposes, they all contribute to 𝗰𝗹𝗲𝗮𝗿 𝗮𝗻𝗱 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀. In 𝗔𝗴𝗶𝗹𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀, these documents may be replaced with 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗕𝗮𝗰𝗸𝗹𝗼𝗴𝘀, 𝗨𝘀𝗲𝗿 𝗦𝘁𝗼𝗿𝗶𝗲𝘀, 𝗮𝗻𝗱 𝗘𝗽𝗶𝗰𝘀, but in 𝗪𝗮𝘁𝗲𝗿𝗳𝗮𝗹𝗹 𝗼𝗿 𝗵𝘆𝗯𝗿𝗶𝗱 𝗺𝗼𝗱𝗲𝗹𝘀, they are still widely used. Which of these documents do you use in your projects? Let’s discuss in the comments! 👇 #BusinessAnalysis #IIBA #BRD #FRD #SRS #RequirementsEngineering #SoftwareDevelopment

LLMOps is about running LLMs like real products with feedback loops, monitoring, and continuous improvement baked in 💯 This visual breaks it down into 14 steps that make LLMs production-ready and future-proof. 🔹 Steps 1-2: Collect Data + Clean & Organize Where does any good model start? With data. You begin by collecting diverse, relevant sources: chats, documents, logs, anything your model needs to learn from. Then comes the cleanup. Remove noise, standardize formats, and structure it so the model doesn’t get confused by junk. 🔹 Steps 3-4: Add Metadata + Version Your Dataset Now that your data is clean, give it context. Metadata tells you the source, intent, and type of each data point: this is key for traceability. Once that’s done, store everything in a versioned repository. Why? Because every future change needs a reference point. No versioning = no reproducibility. 🔹 Steps 5-6: Select Base Model + Fine-Tune Here’s where the model work begins. You choose a base model like GPT, Claude, or an open-source LLM depending on your task and compute budget. Then, you fine-tune it on your versioned dataset to adapt it to your specific domain, whether that’s law, health, support, or finance. 🔹 Steps 7-8: Validate Output + Register the Model Fine-tuning done? Cool, and now test it thoroughly. Run edge cases, evaluate with test prompts, and check if it aligns with expectations. Once it passes, register the model so it’s tracked, documented, and ready for deployment. This becomes your source of truth. 🔹 Steps 9-10: Deploy API + Monitor Usage The model is ready! You expose it via an API for apps or users to interact with. Then you monitor everything: requests, latency, failure cases, prompt patterns. This is where real-world insights start pouring in. 🔹 Steps 11-12: Collect Feedback + Store in User DB You gather feedback from users: explicit complaints, implicit behavior, corrections, and even prompt rephrasing. All of that goes into a structured user database. Why? Because this becomes the compass for your next update. 🔹 Steps 13-14: Decide on Updates + Monitor Continuously Here’s the big question: Is your model still doing well? Based on usage and feedback, you decide: continue as is or loop back and improve. And even if things seem fine, you never stop monitoring. Model performance can drift fast. 📚 Research and Curation Effort: 4 hours If you've found it helpful, please like and repost it to uplift your network ♻️ Follow me, Bhavishya Pandit, to stay ahead in Generative AI! ❤️ #llm #opensource #rag #meta #google #ibm #openai #gpt4 #ml #machinelearning #ai #artificialintelligence #datascience #python #genai #generativeai #huggingface #openai #linkedin #computervision

AI systems become risky when there are no guardrails controlling how they behave at scale. Over the years, I’ve seen teams rush into building AI capabilities— but very few spend enough time designing the systems that keep AI safe, reliable, and accountable. That’s where AI Governance & Security comes in. Think of this as the foundation layer for enterprise AI systems 👇 🔹 Identity & Access Control RBAC, ABAC, IAM, MFA, SSO—control who can access what, and under which conditions. 🔹 Data Protection Encryption, tokenization, masking, secure pipelines—protect sensitive data across its lifecycle. 🔹 Risk Management Risk scoring, bias detection, hallucination monitoring, threat intelligence—identify and reduce AI risks early. 🔹 Monitoring & Observability Real-time tracking, anomaly detection, logging—understand how your AI behaves in production. 🔹 Audit & Accountability Traceability, audit logs, documentation—ensure every decision can be reviewed and explained. 🔹 Compliance & Governance GDPR, EU AI Act, ISO 42001—align AI systems with regulatory and ethical standards. 🔹 Human Oversight HITL, approvals, escalation workflows—keep humans in control for critical decisions. A few critical patterns I’ve seen work in real systems: ✔ Define ownership of AI decisions (RESP) ✔ Enforce policies, don’t just document them ✔ Continuously monitor drift, bias, and anomalies ✔ Always maintain traceability across data and decisions ✔ Introduce human checkpoints for high-risk actions The biggest mistake? Treating AI governance as a compliance checkbox. It’s not. It’s what separates experimental AI systems from enterprise-grade, production-ready AI systems. Because in AI… it’s not just about what the model can do. It’s about how safely, reliably, and responsibly it does it at scale. Follow Vaibhav Aggarwal for more such insights!!

The 2025 GenAI Developer Learning Path: A Step-by-Step Guide After implementing numerous GenAI solutions, here's the proven path to becoming a successful GenAI developer. Follow both tracks simultaneously for the best results: Technical Journey: Start with the Core Foundation - Master Python & ML basics - Build Deep Learning fundamentals - Understand Transformer architecture Move to LLM Fundamentals - Learn HuggingFace ecosystem - Practice fine-tuning techniques - Master prompt engineering Advance to RAG Development - Implement Vector Databases - Build Hybrid Search Systems - Design Multi-Vector Retrieval Tackle Advanced Techniques - Study Constitutional AI - Implement Chain of Verification - Develop Agent Systems Focus on Production - Learn Model Optimization - Deploy Inference Servers - Set up Monitoring Systems Explore Future Tech - Study Multimodal AI - Understand MoE Architecture - Implement Cross Encoders Professional Growth: Start with AI Ethics - Address Bias & Fairness - Ensure Privacy - Practice Responsible AI Add Business Perspective - Analyze Use Cases - Calculate ROI - Handle Stakeholders Manage Risks - Implement Security - Ensure Compliance - Set up Governance Establish Quality - Design Testing Strategies - Track Performance - Collect User Feedback Document Everything - Create API Docs - Map System Architecture - Maintain Clear Guides Develop Leadership - Manage Teams - Plan Projects - Share Knowledge I'd like you to move through both tracks in parallel. The magic happens when technical expertise meets business acumen.

A viral image of an ATM in Ludhiana recently caught my attention - a dangerously steep ramp ending abruptly at a glass door, with a staircase running alongside that leads nowhere. A perfect reminder of a hard-earned lesson in fintech: "Compliance isn’t just a checkbox." Product Managers: You don't want to miss saving 💾 this post for your future reference. This ramp was technically "compliant" - yes, there was a wheelchair access ramp. But it completely missed the purpose of accessibility. People had angry comments on social media about the apathy with which wheelchair-bound customers were treated and how the bank had made a mockery of accessibility. No amount of regulation can account for 'compliance as a checkbox' implementations that are designed to meet the regulation but not serve their intended purpose. It's the same trap I've seen countless fintech products fall into - implementing regulations as mere checkboxes rather than embracing them as design principles. I've experienced regulatory hurdles umpteen times in product launches; in fact, I've never experienced a straightforward implementation that hasn't hit a regulatory roadblock. BUT I can say this confidently: Compliance-first design is the secret sauce that makes the battle easier and less arduous, and inarguably 'faster' IF You just stick to the first principles of building this into your product strategy from day one . Regulations can either slow you down or become your competitive edge. To make compliance your strategic advantage, here's my 3-step playbook: 1/ Design Integration: Make regulatory adherence a natural part of the user experience rather than an afterthought ↳Embed compliance requirements into your initial product design ↳Get feedback from legal and compliance teams, and even the regulator if needed ↳Validate, Test, Iterate, Repeat 2/ Cross-Functional Collaboration: Build bridges between product, legal/compliance teams from day one ↳Involve them early ↳Make compliance & legal stakeholders brainstorm and provide feedback ↳Balance innovation with regulatory requirements using case studies and data to back up assertions instead of getting into crosshairs with them 3/ Validate Early, Validate Often: ↳Test with real scenarios ↳Get early feedback from regulators ↳Regular compliance assessments, no matter what stage of development you are in One golden tip - document everything, err on the side of caution when it comes to building and fostering trust with legal and compliance counterparts. The lesson in one line? Build WITH compliance, not around it. Instead of working around regulations, let's build with them. Because when you design within the right guardrails, innovation doesn't just survive—it scales. What's your strategy for managing fintech compliance? Share below. 👍 LIKE this post, 🔄 REPOST this to your network and follow me, Monica Jasuja

𝐀𝐈 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 & 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐋𝐚𝐰𝐬 𝐟𝐨𝐫 𝐆𝐞𝐧𝐀𝐈 𝐀𝐩𝐩𝐬 Building GenAI Apps for a Global Audience?  Understanding Regional Data Protection and AI laws is not optional, it is foundational. Here is what you need to know: 1. UNDERSTANDING GLOBAL REGULATORY VARIANCE Building GenAI for a global audience requires understanding regional data protection and AI laws. Key Regulations by Region: • EU AI Act: Risk-based AI obligations for certain AI systems and transparency use cases • GDPR (EU): Transparency & Consent • DPDP (India): Digital Personal Data Protection • PIPL (China): Strict Data Localization • CCPA (California): Data Access & Opt-Out • LGPD (Brazil): Local Compliance Rules 2. IMPACT OF THESE REGULATIONS ON YOUR AI TRAINING DATA To build compliant GenAI apps,  Ensure that data used for training AI models follows the regional rules: Data Collection → Processing → Model Training → Deployment Three Core Requirements: a. User Consent: Obtain explicit consent for data collection and use b. Data Minimization: Collect only necessary data for the intended purpose c. Anonymization: Remove personally identifiable information from training data 3. MITIGATING AI ETHICS AND BIAS RISKS AI systems must be fair and ethical, particularly in high-risk areas: a. Fairness: Ensure your AI models don't discriminate, especially in areas like recruitment or finance. b. Bias Mitigation: Regularly test and adjust your models to reduce bias in the outputs. 4. ENSURING TRANSPARENCY IN AI MODEL DEVELOPMENT Transparency is a cornerstone of compliance, especially when your AI impacts users directly: a. Explainability: Protect data in transit and at rest. b. Consent Management: Collect, track, and manage user consent. c. Privacy by Design: Embed privacy into every system layer. 5. MANAGING CROSS-BORDER DATA FLOW GenAI apps often rely on data from various regions, so it's critical to understand data sovereignty laws: a. Data Sovereignty: Follow local laws on where data is stored and processed. b. Data Transfer Agreements: Use SCCs or BCRs for compliant cross-border transfers. THE COMPLIANCE CHECKLIST Before launching GenAI globally, verify: 1. Regional Compliance: • GDPR for EU? (Transparency & Consent) • DPDP for India? (Data Protection) • PIPL for China? (Data Localization) • CCPA for California? (Access & Opt-Out) • LGPD for Brazil? (Local Rules) 2. Training Data: • User consent obtained? • Data minimized? • PII anonymized? 3. Ethics & Bias: • Fairness tested? • Bias mitigation in place? 4. Transparency: • Explainability documented? • Consent management system? • Privacy by design? 5. Cross-Border: • Data sovereignty compliance? • Transfer agreements (SCCs/BCRs)? Each region has different requirements.  Build for the strictest, adapt for the rest. Which regulation applies to your GenAI app?

I've spoken to 300+ SMB leaders who've spent millions on IT partners and end up with broken promises. Ask 3 questions to find one that creates real impact: 1) Do they manage outcomes in addition to supplying talent? Most tech partners follow a basic 'talent outsourcing' model: - Ask a few questions about your needs - Source talent that matches - Hand them over to you to manage There are a couple problems with this approach: → Worst Case: You're left with a developer-only team → Best Case: You get a diverse team but lack the technical background to manage them (This applies unless you're a management savvy CTO.) Talented developers and designers need strategic leadership to build successful software projects. Make sure your tech partner provides said leadership and holds themselves accountable for the final solution. 2) Do they have UX expertise or a design studio? Design is often the most overlooked aspect of software development. An intuitive, user-friendly design - Simplifies complex features - Guides users to solutions quickly It's just as important as making sure the software gets the job done. So, look for an IT partner with proven UX capabilities or a dedicated design team. They should follow a structured process, like: - Conducting user research & interviews - Detailed discovery workshops - Defining user personas - Creating user flows - Wireframing, prototyping, and testing (This is our method at @Incepteo.) Writing code should NOT start before finalizing design to avoid re-coding or re-designing. Last but not least: 3) Do they provide CTO advisory? The Project Manager is usually responsible for the software's timeline, budget and scope – but having a CTO prevents you from facing many potential roadblocks. They: - Spearhead strategy and implementation - Review the software's design and structure - Share on the dos and don'ts based on past experience The best part is: they don't need to be present full-time. 1 hour of CTO advisory per month is enough to help most businesses move in the right direction. — If your company's investing time and money into a solution, make sure your partner provides the talent, design, and advisory for you to succeed. Ask these questions on the vendor selection call to see if they could be a fit. And, if you're tired of failed projects and ineffective solutions, send me a message on LinkedIn so we can chat about your requirements and needs. (We answer YES to all 3 questions 😄)

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.