Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation
Data Engineers in Toronto January 2027 Semimonthly Meeting Topic: Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation Abstract: Every data engineer knows the 2 AM pipeline failure, the one nobody notices until Friday's report is wrong. In this session, we break down five AI agents that are changing how data teams operate: from monitoring pipelines 24/7 and catching schema drift at ingestion, to closing the gap between a production failure and its root cause in minutes. We'll walk through real implementation patterns, including a baseline-learning monitoring agent and a tool-use driven incident response loop, and discuss what the shift to agentic data engineering actually means for the way teams are built and how engineers grow. Whether you're evaluating agents for your platform or already running them in production, you'll leave with concrete patterns you can apply immediately. Speaker: Varun Joshi, Senior Data Engineer at AWS Speaker Profile: Highly motivated and results-oriented Data Engineer with 12+ years of experience in designing, building,and optimizing scalable data pipelines and architectures.Proven expertise in data warehousing, ETL/ELT processes, and cloud platforms. Passionate about leveraging Artificial Intelligence (AI) and Machine Learning (ML). Designed and deployed AI-driven Data solutions, integrating LLM-powered coding assistants into Data Engineering to produce AI solutions for customers. Focused on leveraging LLMs and advanced engineering to build scalable, secure, and trustworthy platforms, resulting in significant efficiency gains, reduced on-call burden,and improved customer trust. Driving AI adoption across teams to enhance productivity, streamline deployments, and improve end-user experience. The meeting is over Microsoft Teams, and the joining link is https://vip.dataengineersintoronto.org/webinar See you at the meeting!Beyond the Prompt: Tuning Fabric Data Agents for Flawless Results
Data Engineers in Toronto August 2026 Semimonthly Meeting Topic: Beyond the Prompt - Tuning Fabric Data Agents for Flawless Results Abstract: As organizations race to deploy AI Agents, it quickly becomes clear that standard prompt engineering isn't enough. To achieve production-grade accuracy, you have to go beyond the prompt and fundamentally change how you feed your data layers to the AI. In Microsoft Fabric, your choice of data artifact directly dictates your agent's reasoning style and native language. In this session, we will deep-dive into how Fabric Data Agents interact with four distinct data paradigms: Semantic Models, Ontology, Data Warehouses, and Lakehouses. Moving past high-level theory, we will practically map the three pillars of data agent optimization across the data paradigms: Agent Instructions (System Prompts) Data Source Descriptions (Grounding) Example Queries (Few-Shot Prompting) You will walk away with an actionable blueprint showing you exactly how to write context rules, embed semantic metadata, and supply technical query examples to ensure your Fabric Data Agents deliver flawless, trusted results every time. Speaker: Thimantha Vidanagamage, Data Engineer at OMERS, Canada Speaker Profile: Thimantha has over 8 years of experience in Data and AI. He currently works as a Senior Data Engineer at OMERS in Canada, where he plays an active role in the organization’s adoption of Microsoft Fabric. He holds multiple Microsoft certifications, including Microsoft Certified: AI Transformation Leader and Fabric Data/Analytics Engineer Associate. Thimantha is also a co-organizer of the Toronto Data Professionals Community. The meeting is over Microsoft Teams, and the joining link is https://vip.dataengineersintoronto.org/webinar See you at the meeting!Building Collaborative AI Agents Using AI Foundry Agent Service
Data Engineers in Toronto September 2026 Semimonthly Meeting Topic: Building Collaborative AI Agents Using AI Foundry Agent Service Abstract: As enterprise AI adoption grows, many real-world scenarios can no longer be solved by a single conversational agent. Instead, they require multiple specialized agents that collaborate, share context, and delegate responsibilities intelligently. This session focuses on how to design and implement multi-agent architectures using Azure AI Foundry’s Agent Service. We’ll start by breaking down when and why multi-agent systems make sense, then walk through a practical scenario where agents are assigned clear roles such as data retrieval, reasoning, and action execution. Using AI Foundry Agent Service, you’ll see how these agents are created, connected, and orchestrated to work together while maintaining context and control. Rather than focusing on theory, the session emphasizes practical design patterns, architecture decisions, and implementation considerations, including agent boundaries, communication flows, and scaling agent-based solutions safely in enterprise environments. By the end of the session, attendees will have a solid understanding of how to move from a single-agent approach to a collaborative, multi-agent model and how to apply these patterns using AI Foundry in their own solutions. Key Takeaways: - Understand when multi-agent architectures are the right choice - Learn how to design role-based agents using AI Foundry Agent Service - Explore real-world coordination and orchestration patterns - Gain practical guidance for scaling and governing multi-agent systems - Leave with a reusable architecture blueprint for enterprise AI agents Speaker: Mitul Tailor, Snr. Data & AI Engineer Speaker Profile: am a Senior Data & AI Engineer at Long View Systems, specializing in Azure services, data engineering, and AI-driven solutions. With expertise in Azure Data Factory, Fabric, Copilot Studio, and data migration, I have successfully delivered scalable and efficient data solutions. I hold a Post Graduate Certificate in Generative AI, a PG Diploma in ML & AI, and a Bachelor's in Computer Science. I am DP-600 & AI-102 certified, demonstrating my expertise in Microsoft Azure Enterprise Data Analyst solutions. Additionally, I have a strong understanding of Retrieval-Augmented Generation (RAG) AI and Azure AI Foundry, exploring how these technologies can be leveraged for intelligent automation and data-driven insights. Recognized by clients for my clear and precise development work, I am passionate about automation, AI, and robotics. Outside of work, I love to travel and have a deep interest in cars.
