Lakshmanan Sethu Sankaranarayanan

How did we used Domain Driven Design  concept to break down our monolithic system into bounded contexts, and modernize it into cloud-based microservices? Also, how did we implement the bubble context architecture pattern? Read our Engineering Technical blog Credits : Danish Garg https://lnkd.in/dhPSDf4V

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At AI World Vegas, Oracle’s Sharat Chander told The Times of India that young engineers can stay relevant by strengthening their fundamentals — from concurrency and performance tuning to API design — and using AI tools to accelerate, not replace, learning. https://lnkd.in/eAzFdRc5

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Introducing India’s First AI DBA Agent – Built for Real Production Environments After 6+ years as a SQL Server DBA working on performance tuning, HA/DR, backups, monitoring, and critical incidents… I asked myself one question: Why are we still manually firefighting database issues in 2026? So I built something different. 🤖 Introducing: AI DBA Agent An Autonomous Agentic AI system that monitors, diagnoses, and assists in resolving SQL Server issues — 24/7. This is not a chatbot. This is a production-grade AI assistant for DB environments. What It Solves 1.Detects blocking, deadlocks & long-running queries 2.Analyzes wait stats & CPU spikes 3.Identifies missing / unused indexes 4.Monitors backup failures automatically 5.Tracks storage growth & predicts risk 6.Generates structured incident reports 7.Suggests safe performance tuning scripts 8.Works in read-only or approval-based execution mode 9.Supports multi-database environments Imagine having a virtual DBA monitoring your servers every 5 minutes. No fatigue. No missed alerts. No delays. Who Is This For? • Startups without 24/7 DBA coverage • saas.group companies running SQL workloads • Managed service providers • Mid-size enterprises looking to reduce downtime • CTOS who want proactive monitoring Pricing Model: Simple SaaS subscription. But here’s the part I’m confident about: --->1 Month FREE Trial No commitment. No hidden charges. If it genuinely helps your organisation — you continue. If not — you walk away. I believe in value first. Why I Built This: Downtime is expensive. Manual monitoring is reactive. DBA burnout is real. AI should not replace DBAs. AI should empower them. This system acts like an AI Copilot for database teams. If you're interested in: Seeing a demo Joining the free trial Becoming an early adopter Partnering as a reseller Comment “AI DBA” or DM me. Let’s make database management intelligent. #AgenticAI #SQLServer #AIForEnterprise #DatabaseManagement #StartupIndia #SaaS #AIProducts #CTO #CEO #CIO #TechLeadership #DigitalTransformation   #EnterpriseIT #ITStrategy #BusinessInnovation #SaaSProduct   #EnterpriseAI #AgenticAI #AIForBusiness #AIAutomation   #CloudComputing #CloudInfrastructure #SQLServer   #DatabaseManagement #DevOps

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🚀 Excited to share the Azure AI Travel Agents sample application from Microsoft! This flagship open-source project showcases the next level of GenAI in travel solutions: orchestrating multiple specialized AI agents for real-time, personalized travel planning and assistance. ✨ What makes it unique? ☑️ Agentic architecture: Multiple AI agents (each with domain expertise), coordinated by LlamaIndex/TSC# ☑️ Powerful integrations: Built on Azure OpenAI, functions, communication services, Cosmos DB, and more ☑️ Hands-on sample: Ready to deploy and extend for real-world travel, hospitality, or concierge solutions ☑️ Truly cross-functional: Supports .NET, Python, TypeScript/JavaScript, and Java components for end-to-end workflows Whether you’re a cloud architect, AI engineer, or developer, this repo is a fantastic resource for learning about multi-agent orchestration, serverless container deployment, and next-gen AI use cases. 🔗 Check out the repo: https://lnkd.in/etRzejRx Shoutout to the MS team and all contributors for enabling innovation in the travel tech space! #AzureAI #OpenSource #TravelTech #AIOrchestration #MCP #CloudNative #GenAI #Developers

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Oracle and AWS have expanded their collaboration to make the Oracle Database@AWS service generally available in the US. InfoWorld shares all the details. https://lnkd.in/gus-EUFd

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https://lnkd.in/g2XXZYMq Gartner didn’t just rank a CDP Leader — it redefined the category. The 2026 Gartner Magic Quadrant makes something very clear: Customer Data Platforms are no longer marketing tools. They are becoming enterprise context engines — the foundation on which agentic AI actually works. That shift matters. At MIMIT Health, this is not a theoretical discussion. The agentic enterprise architecture highlighted in today’s article was designed and implemented by CIMSS Innovative Solutions as a real, production-grade operating model. CIMSS architected Salesforce Data 360 as the unifying context layer across clinical systems, labs, imaging, patient communications, and care workflows — intentionally wrapping around legacy EHRs rather than replacing them. MuleSoft decouples source systems. Data 360 harmonizes and governs context. AI agents summarize medical histories, support care coordination, and activate workflows across omnichannel touchpoints. Slack enables real-time, context-aware clinical collaboration. This is not a pilot. It is a wall-to-wall Salesforce implementation, delivering measurable clinical and operational outcomes — including 24/7 patient access, continuity of care, and reduced downstream utilization. The article captures this broader shift well: AI does not fail because models are weak — it fails because context is fragmented. Without harmonized, governed, enterprise-wide context, agents cannot reason or act safely at scale. I want to specifically thank Rahul Auradkar and the Salesforce Data Foundations leadership team for articulating — and executing on — the vision of Data 360 as a true context engine, not just a CDP. That clarity of direction is what enables implementations like this to move beyond experimentation into real-world impact. At CIMSS, our focus is exactly this: helping organizations design, build, and operationalize agentic enterprises where data foundation, governance, and activation function as a single system — not disconnected tools. The takeaway for enterprise leaders is simple: Stop asking which AI model you need. Start asking whether you have the context architecture AI depends on. Grateful to see this evolution recognized — and excited about what comes next.

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