Vidhai AI Solutions’ Post

AI SRE doesn't replace the person on call. It lets engineers start on page 5 of an investigation instead of page 1. The copilot handles tedious correlation work so engineers can focus on decisions. During my time at Confluent and Dropbox, I learned that the most complex outages were the ones with context scattered across different layers of the stack. A copilot bridges that gap. But it only works if you build your observability foundation correctly. In my ClickHouse post, I explain how to build this foundation, one that reduces the time it takes to identify the root cause of an incident from hours to minutes. Read the full article here: https://lnkd.in/gq4qMuMR #AI #SRE #ClickHouse #Observability #ZenithAI

AI workloads don’t fail because of models — they fail because storage can’t scale, feed, or adapt. Object storage wins for AI because: • Flat namespace = infinite scalability • Metadata-rich data for faster training & inference • Optimized for unstructured data (images, video, logs, telemetry) • Designed for cloud-native & hybrid AI pipelines Modern AI needs storage built for parallelism, not hierarchy. Architect accordingly. #AIInfrastructure #ObjectStorage #MLops #CloudNative #DataPlatforms #DigitalTransformation #TechLeadership

Having worked with databases for many years, I have seen firsthand how critical data and database infrastructure are to making AI actually succeed, whether it’s powering RAG pipelines, AI agents, or large-scale inference in production. As I speak with more and more customers and partners, it’s becoming clear that this holds true across the board. One theme keeps coming up: moving AI into production stresses infrastructure much earlier than most teams expect. The State of AI Infrastructure 2026 report from Cockroach Labs echoes this same sentiment. The data highlights how quickly downtime costs add up, how soon infrastructure limits start to show, and why databases are becoming a critical pressure point as AI workloads scale. If you’re building for AI in production (not just experimenting), this is a great snapshot of where infrastructure decisions are headed and what teams will need to prioritize to support AI at scale. 📘 The State of AI Infrastructure 2026: https://lnkd.in/eZwS-rRA #AI #AIInfrastructure #Databases #DataInfrastructure #MLOps #RAG #AIAgents #ProductionAI #DistributedSystems #CloudInfrastructure

The "Agentic AI" era is here, and it’s disrupting everything we know about data. Databricks just released a definitive guide on the Types of AI Agents ,from task specific tool users to complex multi agent systems. The goal? Moving past simple chatbots to autonomous systems that actually do the work. At Cosmos Thrace, we see this as the single biggest opportunity for our clients to drive operational efficiency. As a Databricks Implementation Partner, we specialize in taking these high level agentic architectures and turning them into real world solutions that solve your most complex data bottlenecks. If you’re wondering how to structure your data intelligence platform for this shift, this new breakdown from Databricks is the perfect place to start: Types of AI Agents: Definitions, Roles, and Examples Ready to move from pilot to production? Let’s talk about how Cosmos Thrace can help you build your first fleet of AI agents. https://lnkd.in/gewVr-ze #Databricks #AIAgents #DataIntelligence #CosmosThrace #GenerativeAI #DataEngineering

Why 90% of Agentic AI Demos Fail in Production (And How to Fix It) 🛑 The "POC Trap" is real. We’ve all seen the dazzling Agentic AI demo:A model plans a task, calls a tool, and solves a problem. It looks like magic. But then you try to scale it. Suddenly, you’re hit with the State Management Crisis: ❌ Agents losing context mid-session. ❌ Tool calls failing without a recovery path. ❌ Concurrency bottlenecks that spike latency and costs. A simple POC is stateless. A production agent is a complex, multi-step state machine. If you’re building for the enterprise, you don't need a better LLM. You need a better Orchestrator. In my latest article, I dive deep into how Amazon Bedrock AgentCore is solving the state management crisis, moving us from brittle demos to resilient, production-ready digital employees. We’re moving beyond the hype. It’s time to build for scale. Read the full breakdown here: --- #AgenticAI #AWS #GenerativeAI #Bedrock #MLOps #EnterpriseAI #ShubhamVelrekar #AWSAIConclave2026

“Just saying one thing will do it all is like saying we know the answer to a question even before you’ve asked it." In this article from The New Stack, Yugabyte CEO Karthik Ranganathan and other industry experts share their thoughts on AI agent collectives, ensemble models of LLMs, Retrieval Augmented Conversation (RAC), and GPU clouds, as companies grapple with a way to centralize an increasingly fragmented data ecosystem.💡 https://lnkd.in/eefKNwG3 #distributedsql #RAG #MCPServer #Postgrescompatible #ai #GenAI #database #cloudnative #opensource #yugabytedb #RAC #LLM #GPU

Last night I wrapped up watching AWS re:Invent, and one message came through clearly: AI has officially moved from experimentation to execution. This year wasn’t about flashy demos, it was about operational AI: • AI embedded directly into everyday workflows • A strong push toward agentic systems that can act, not just respond • Massive investment in infrastructure to make AI more accessible and cost-effective • Clear emphasis on data quality, governance, and trust as prerequisites for scale What stood out most is how AWS framed the future: AI success won’t come from models alone, it will come from modern data foundations, strong partner ecosystems, and responsible deployment. We’re standing at the edge of the next wave, where AI becomes part of the operating model, not a side project. #AWSreInvent #ArtificialIntelligence #GenerativeAI #AgenticAI #CloudComputing #DataModernization #ResponsibleAI #CloudEcosystem

The most important part of Machine Learning is something nobody talks about. Not the model. Not the architecture. Not the accuracy chart. It’s the data pipeline. Before a model learns anything, a silent system is doing the hardest work: collecting data cleaning it validating it transforming it delivering it to training & inference Here’s a hard truth from real-world ML: 80% of machine learning is data engineering. Without a strong pipeline: models break in production predictions drift debugging becomes impossible A modern ML pipeline handles: → ingestion → validation → transformation → feature storage → training & serving → monitoring Great models are replaceable. Great pipelines are not. If you’re serious about production ML, stop focusing only on notebooks. Start building systems. Because in real life: Great pipelines beat great models. Always. #MachineLearning #DataEngineering #MLOps #AI #SoftwareEngineering

In the race to ship GenAI features, I’m seeing a recurring pattern in production architectures. Teams spend weeks debating which LLM to use or how to "perfect" a system prompt, but almost zero time on the Ensemble Strategy or Data Retrieval Efficiency. The "Brute Force" approach—using the most expensive frontier model for every single API call—is the fastest way to kill a project's ROI once it scales. As we build out platforms like Power Assist, I’ve realized that a "Production-Grade" Agentic AI system requires a shift in mindset: The Model Ensemble: Not every task needs GPT-4o or Claude 3.5. A routing layer should handle intent classification with a "small" model (like Llama 3 or Phi), reserving the expensive reasoning for the 20% of requests that actually need it. Data Fetching is the Bottleneck: We talk about RAG, but we don't talk enough about optimizing the Postgres/Vector search backends. If your data retrieval isn't surgical, you're paying "Token Tax" on irrelevant context. The Scalability Wall: A prototype that costs $0.50 per request looks great in a demo. That same architecture becomes a liability when you're hitting 100k requests/day. My take: Real AI Engineering isn't just about the "AI." It’s about building a robust system of orchestration, cost-governance, and explainable data flows. If you aren't looking at your per-request cost and model-routing logic today, your scale-up will be a wake-up call tomorrow. #GenerativeAI #LLMOps #Azure #Databricks #DataEngineering #CloudArchitecture #AIStrategy

🚀 Working with Generative AI in regulated enterprise environments Recently, I’ve been hands-on building Retrieval-Augmented Generation (RAG) solutions that help analysts make sense of large volumes of internal policies, regulatory guidelines, and historical documents—while keeping outputs grounded, auditable, and explainable. Some key learnings from this journey: RAG is not just about embeddings + LLMs — metadata, document quality, and retrieval logic matter just as much. Prompt design needs to align with governance, traceability, and risk controls, especially in banking and healthcare domains. MLOps principles (versioning, monitoring, evaluation) are just as important for prompts and RAG pipelines as they are for traditional ML models. Excited to continue exploring how GenAI + classical ML + strong data engineering can deliver real, production-ready impact—not just demos. #GenerativeAI #RAG #LLM #EnterpriseAI #MLOps #Azure #Databricks #MachineLearning