Kamal Hathi

Great news for Spanner and BigQuery users. Datastream now provides a seamless, serverless bridge from Spanner’s transactional power to the high-performance analytical capabilities of BigQuery and BigLake. Get the most scalable and available database that runs Google with BigQuery and BigLake’s serverless AI-Native Lakehouse!

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If you’ve seen my explainer video on MCP and would now like to see actual practical things you can do with it, _watch this video_. Sami walks you through using the Confluent MCP server to do things in Confluent Cloud under voice control. Now imagine taking this demo and extending it to run from your own agentic microservice. This stuff is all very, very doable now…

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While below update might appear a new release or a feature been made available. I will try to elaborate on what it can do for business in digital era. Its not a regular release. Its a game changer Here are key areas impacting business where we are working with customers even before the launch of Tableflow for early access. Saving AI models from hallucinations Hyper Personalisation Realtime recommendation engine Realtime Fraud Prevention Quality Improvement in manufacturing Energy optimization Proactive maintenence Connected Cars, realtime action Cyber Defense Would really encourage practioners and think tanks to share their comments with expanding above pointers. Look forward to some insightful comments. #tableflow #flink #unitycatalog #Kafka #Confluent #AWS #deltalake

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LLM serving doesn’t have to be slow or expensive. In this new Domino Blueprints guide, Principal Solution Architect Sameer Wadkar shares how to deploy LLMs as model endpoints—without embedding huge binaries in your model image. Learn how to fine-tune, register, and serve LLMs efficiently using Domino Model Registry and shared datasets. 📘 Stay fast, lean, and compliant: https://gag.gl/Bj70bg #LLMOps #MLOps #ModelDeployment #AI

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Today let's talk about Parallel Fan Out/ Concurrent Agentic Design pattern and when parallel agents go rouge What’s the Concurrent Orchestration Pattern? Imagine a scenario where multiple AI agents—each with its own lens or specialty—tackle the same task simultaneously. Instead of a single, step-by-step chain, tasks fan out to different agents in parallel. Then their outputs are merged or aggregated for the final answer. It’s the AI equivalent of a brainstorming session where everyone chips in together. This pattern thrives when you need diverse insights or speed—think ensemble reasoning or reaching a verdict faster. This pattern is commonly used in agentic RAG. Here’s how you can apply it in a Retrieval-Augmented Generation (RAG) system: Step 1: Break the user’s query into smaller sub-queries (e.g., “Define concept X,” “List use cases,” “Give examples”) and map out their dependencies. Step 2: Run those sub-queries in parallel—agents fetch context or process each in isolation (e.g., document retrieval, summarization, external tool usage). Step 3: Once all agents have results, aggregate the findings into a single, coherent response. This technique lets you parallelize independent parts, reducing latency while maintaining clarity—especially effective when sub-queries don’t depend on each other. Super Important : Try to avoid this pattern and use sequential execution even if it's slow, but it might yield a good result, below is the reason As Cognition [the company behind devin] warns in a famous blog post “Don’t Build Multi-Agents”, things can go sideways fast if agents don’t share context or coordination is weak. When agents operate in isolation—making decisions based solely on their own view—the final result might be fragmented, contradictory, or just plain incoherent. Think two agents building different puzzle pieces that don’t fit. The core issues: Context fragmentation: Each agent works in a silo, leading to mismatched assumptions. Implicit decisions: Agents’ outputs reflect unspoken choices that may clash when merged. Coordination complexity: Without strong orchestration, integration becomes error-prone. References : 1. Concurrent Execution Pattern : https://lnkd.in/gZybgm2s 2. LLM Compiler whitepaper : https://lnkd.in/gWwGJhbi 3. Coginition blog Don't build multi agents which shows parallel execution can yield agent drift : https://lnkd.in/gkDKC4TP

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In this world of Agents.. how do you ensure that your agent is doing what it's designed to do? Agent Evaluation is one of the core capabilities required for Explainable AI. @Snowflake #Cortex #Agents Evaluation is now GA.

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According to Gil Luria, head of technology research at investment bank DA Davidson, these companies benefit from the growing demand for #AI #software tools, offering essential services for expanding AI capabilities. https://lnkd.in/gUgiQVHU Snowflake Datadog JFrog

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Modern data stacks rarely live in one system. That’s why we've expanded Cortex Code CLI beyond Snowflake workflows to support any data, anywhere, starting with dbt and Apache Airflow® patterns natively. From schema-aware discovery to DAG generation and debugging, it brings deeper context to multi-system data engineering – all while maintaining enterprise governance and model flexibility. And, we're opening access with a self-serve subscription model, so you can get started without a Snowflake account. Details here: https://lnkd.in/gf23cJA5 

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