Run a hands-on hackathon that ships real solutions.

This Hack in a Day learning experience enables participants to design and implement an Agentic DevOps solution that assists engineering teams in diagnosing and responding to CI/CD pipeline and deployment failures. The lab guides participants through building an AI-powered DevOps agent that monitors Azure DevOps pipeline executions, analyzes failure logs, classifies root causes such as infrastructure, configuration, test, or code issues, and generates actionable remediation recommendations. By leveraging Azure DevOps, Azure Monitor, and Azure OpenAI, participants gain hands-on experience in reducing manual troubleshooting effort, improving release reliability, and accelerating recovery times through intelligent agent-based reasoning.

This Hack in a Day lab focuses on building an Agentic Infrastructure Operations (InfraOps) assistant that monitors infrastructure health, detects anomalies, and recommends operational actions. Participants design an AI agent that ingests infrastructure metrics, logs, and configuration data to identify common operational issues such as capacity constraints, misconfigurations, or availability risks. The lab demonstrates how AI agents can augment infrastructure teams by providing proactive insights, guided troubleshooting, and recommended remediation steps across cloud environments.

Build an agentic security operations workflow on Microsoft Sentinel. Stand up the Sentinel MCP server for entity analysis, generate and validate KQL threat hunts with AI, run a multi-model incident scan, auto-create remediation playbooks, and close incidents with a Security Copilot ATT&CK report.

Sales teams rely on fragmented information from CRM systems, news, financial updates, and market sentiment sources. Manual data aggregation is inefficient and often results in incomplete account understanding before customer meetings.

Clinical researchers must manually review large volumes of unstructured medical literature, clinical trial reports, and case studies to identify treatments, outcomes, and regulatory information. This process is slow, error-prone, and delays evidence-based decision-making.

This Hack in a Day lab focuses on building an autonomous Data Security Agent that identifies sensitive data, detects risky access patterns, and recommends security controls. Participants work with sample datasets and metadata to classify sensitive information, analyze permissions, and generate actionable compliance insights. The experience demonstrates how AI agents can support continuous data protection and compliance in modern cloud environments.

Telecom operators manage massive volumes of telemetry and incident data but often respond reactively to network issues. Root cause analysis and remediation are time-consuming, leading to increased downtime and service degradation.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help IT and platform teams understand how conversational AI can modernize enterprise IT support. The lab guides participants through an end-to-end, production-style solution that builds an intelligent IT Support Copilot capable of answering common support questions, guiding users through troubleshooting steps, and automatically escalating issues when required. Participants work with real-world IT scenarios such as password resets, VPN connectivity issues, and hardware troubleshooting, using knowledge base documents to ground AI responses and workflows to automate ticket creation. By leveraging Microsoft Copilot Studio...

This Hack in a Day experience enables participants to build an Azure SRE Agent designed for multi-tenant managed services environments. The agent ingests alerts and telemetry from multiple Azure subscriptions, correlates related signals into incidents, identifies probable root causes, and recommends remediation actions. Participants simulate common MSP scenarios such as VM outages, performance degradation, and SLA risks to gain hands-on experience in AI-assisted cloud operations.

This is a real-time analytics and automation learning experience designed to help organizations gain instant insights from streaming events using Microsoft Fabric RTI. The lab guides participants through building a complete pipeline that streams synthetic events, processes and models data with Eventstream and Eventhouse, and queries using KQL. Data is exposed via OneLake/Lakehouse and visualized through Real-Time Dashboards, while threshold-based alerts and automated actions are configured using Data Activator (Reflex). Participants gain hands-on experience creating live, auto-refreshing dashboards that enable real-time monitoring, rapid response, and proactive decision-making across business operations.

This is a hands-on, production-focused learning experience designed to help enterprises move from basic LLM demonstrations to intelligent, goal-driven AI agents. The lab guides participants through building task-oriented agents using Microsoft Foundry, covering project setup, model integration, vector stores, and Retrieval-Augmented Generation for grounded reasoning. By integrating Azure AI Search, Bing grounding, and observability through OpenTelemetry and Azure Monitor, participants gain visibility into agent behavior, performance, and reliability. Through practical multi-agent implementations using Semantic Kernel and AutoGen in health and fitness scenarios, attendees learn how to design, orchestrate, and operate scalable, well-governed AI agents suitable for real-world enterprise use.

This is a hands-on, AI-driven content processing learning experience designed to help enterprises automate the extraction and management of high-volume, multi-format documents. The lab guides participants through building a pipeline using Azure Document Intelligence, GPT Vision, and Microsoft Foundry. Documents are ingested from Blob Storage and processed with OCR and GPT Vision for multi-modal understanding. Extracted data is transformed into JSON using schema mapping, enriched with confidence scoring, and flagged for human validation when confidence falls below a defined threshold. Structured outputs are stored in Azure Cosmos DB, while Container Apps host API workflows. Participants...

This is a business-intelligence–focused learning experience designed to help teams transform raw analytics into polished, executive-ready insights. The lab walks participants through enhancing a pre-built Power BI report with consistent branding, improved visual storytelling, and advanced interactivity using features such as conditional formatting, custom visuals, mobile layouts, selection panes, and bookmarks. Participants then publish the report to the Power BI Service, organize content within a workspace, and build an executive dashboard by pinning key visuals, applying filters, and leveraging capabilities like Q&A, Quick Insights, and optional alerts. Attendees gain practical skills to deliver clear, actionable...

Enterprises suffer from siloed knowledge across IT, HR, Finance, and Operations. Employees waste hours searching for policies, SOPs, past tickets, and reports. Data fragmentation leads to poor decision-making and duplicated efforts.

CX teams collect vast volumes of unstructured feedback emails, chat transcripts, social media posts without the capacity to process and act on them quickly. Manual review delays issue detection and insights, while disconnected tools prevent sentiment tracking across channels. The organization lacks an AI-driven approach to analyze trends and improve customer experience proactively.

Customer service teams handle fragmented emails, chats, CRM tickets, and feedback. Identifying common trends, summarizing complaints, or predicting escalations manually is challenging. Agents need a Copilot that analyzes multi-channel feedback, classifies issues, and suggests resolutions.

Customer care teams spend extensive time responding to repetitive inquiries such as order status, refund policies, product details, and technical troubleshooting. Manual triage of customer emails and chats causes long response times, inconsistent quality, and agent burnout. Traditional CRM tools often lack integrated automation, making it difficult to scale customer engagement efficiently.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help manufacturing and technology professionals understand how generative AI can transform operational data analysis. The lab guides participants through an end-to-end, production-style solution that ingests machine log data, enables semantic search, and delivers conversational, AI-driven insights using a Retrieval-Augmented Generation (RAG) approach. Participants work with real-world manufacturing signals such as temperature, vibration, machine status, and downtime events to identify trends, detect anomalies, and answer natural-language queries grounded in actual production data. By leveraging Microsoft Foundry (Azure OpenAI), Azure AI Search, Azure Blob...

This is a security-focused, production-ready AI deployment learning experience designed to help organizations operationalize AI solutions safely. The lab guides participants through deploying a secure AI environment using Microsoft Foundry with private VNETs, private endpoints, Azure Key Vault, Azure Storage, and Entra ID. Participants configure network isolation for all services, disable public access, and deploy a sample chat-based AI application within the secured network. Deployment is automated using Azure Developer CLI (AZD) and Azure Verified Modules (AVM), and secure connectivity is validated using Bastion or Codespaces. Attendees gain hands-on experience in implementing compliant, well-architected AI...

This is a practical, end-to-end learning experience designed to help professionals build reliable and accurate AI applications using Retrieval-Augmented Generation. The lab guides participants through creating a custom RAG solution with the Microsoft Foundry SDK, starting from project setup and model deployment through to vector indexing and retrieval. By integrating Azure AI Search for grounded responses and Azure Monitor for unified observability, participants learn how to reduce hallucinations and improve response relevance. Through hands-on implementation of ingestion, retrieval, and generation pipelines, along with quality evaluation using Azure AI Evaluators, attendees gain the skills needed to...

Build an agentic retail analytics stack on Microsoft Fabric. Ingest a large dataset into a Delta Lake lakehouse, benchmark a GPU-accelerated warehouse, query it in natural language with Fabric IQ, build a data agent that alerts to Teams, and surface live KPIs in a dashboard.

Finance teams handle large volumes of invoices, statements, and audit reports, often spread across emails, PDFs, and spreadsheets. Manual extraction of key fields (totals, supplier info, tax) consumes hours and risks errors. Compliance validation and reconciliation remain manual, slowing reporting cycles. Disconnected workflows limit visibility and data-driven financial decisions.

Month-end reconciliation requires scanning invoices, spreadsheets, bank statements, and GL records manually. Multiple sources make reconciliation slow, error-prone, and dependent on finance analysts. They need an AI Copilot that extracts data, aligns mismatches, flags anomalies, and summarizes variances.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help finance and technology teams understand how AI can enable proactive financial analysis and risk management. The lab guides participants through an end-to-end, production-style solution that builds an intelligent Financial Insight & Risk Advisor Copilot capable of analyzing financial reports, summarizing performance trends, classifying business risk levels, and automatically escalating high-risk findings to leadership. Participants work with real-world financial documents such as quarterly reports and cash flow statements, using conversational AI to identify anomalies, pressure indicators, and emerging risks. By leveraging Microsoft...

Build an AI-native fraud detection system on HorizonDB. Provision a zone-redundant cluster, index transaction embeddings with DiskANN, detect fraud rings with Apache AGE graph queries, run SQL-native LLM calls for hybrid search, and expose a fraud-check tool to a Microsoft Foundry agent.

HR departments manage thousands of policy documents, FAQs, and survey data scattered across SharePoint, emails, and files. Employees face delays in finding accurate policy information, and HR analysts spend hours summarizing engagement results manually. This fragmentation leads to low HR efficiency and inconsistent communication across departments.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help finance and technology professionals understand and implement intelligent document processing for invoice automation. The lab guides participants through an end-to-end, production-style solution that automates invoice data extraction, validation, storage, and visualization using Azure AI services. Participants work with real-world invoice formats such as PDFs and scanned images to extract structured financial data including vendor details, invoice numbers, dates, line items, subtotals, and tax amounts, while applying business rules to ensure data accuracy and completeness. By leveraging Azure AI Document Intelligence, Azure...

Large enterprises face massive volumes of repetitive IT support requests - password resets, connectivity issues, software access, and device provisioning. Traditional manual triage and ticket systems cause slow response times and low user satisfaction. Support engineers often navigate multiple tools without unified visibility, leading to higher mean time to resolution (MTTR). Existing automation lacks conversational accessibility and contextual reasoning, limiting its ability to resolve issues autonomously.

IT departments frequently face overwhelming volumes of repetitive support requests password resets, software access, connectivity issues, or device setup queries. Traditional manual helpdesk processes lead to delayed responses, inconsistent resolutions, and poor visibility into support trends. IT agents often switch between multiple systems to log tickets, track issues, and update users, which reduces productivity and user satisfaction.

IT operations teams handle logs, alerts, incidents, and tickets across multiple tools (Azure Monitor, ticketing systems, email alerts). Manual triage slows down response times and lacks consistent analysis. IT teams want an intelligent Copilot that can interpret logs, identify root causes, and trigger actions autonomously.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help enterprise teams understand how AI can transform internal knowledge management and employee productivity. The lab guides participants through an end-to-end, production-style solution that builds an intelligent Knowledge Navigator Agent capable of searching across 40+ internal documents spanning HR, Finance, IT, Procurement, Sales, Support, and Operations. Participants work with real-world employee queries such as policy lookups, process guidance, and document access, using conversational AI to retrieve accurate information, share documents, and trigger automated actions. By leveraging Microsoft Copilot Studio, SharePoint, agent flows,...

Managing IT assets manually across their lifecycle from procurement to decommissioning often results in inaccurate inventories, missing equipment, and compliance issues. Tracking devices, software licenses, and ownership status through spreadsheets is inefficient and error-prone. Without integrated visibility, IT operations face audit gaps, delayed provisioning, and poor cost tracking.

Recruitment processes often involve manual screening of resumes, repetitive communication with candidates, and uncoordinated interview scheduling. HR teams struggle with tracking applicant progress, managing data silos, and ensuring a consistent candidate experience. This inefficiency delays hiring and increases administrative overhead.

In large organizations, data and processes often exist in silos across IT, HR, finance, and customer operations. Employees frequently switch between applications to retrieve information or perform tasks, causing inefficiency and poor collaboration. Lack of unified visibility hinders quick decision-making and operational agility.

This is a practical, enterprise SQL modernization learning experience designed to help organizations efficiently update legacy SQL environments for cloud adoption. The lab guides participants through building a multi-agent SQL modernization pipeline using Microsoft Foundry and Azure OpenAI. Users upload legacy SQL queries and specify the target dialect, while agents perform translation, validation, optimization, and semantic error checking. Orchestration logic is deployed within container apps, and Azure Cosmos DB stores results, validation checks, and error logs. A built-in retry and review mechanism ensures accuracy. Participants gain hands-on experience in automating SQL modernization at scale, reducing...

This is a hands-on, intelligent automation learning experience designed to help enterprises streamline complex, multi-step business operations using AI-driven agents. The lab guides participants through designing a multi-agent automation engine with Azure OpenAI and Semantic Kernel, where specialized agents such as extraction, validation, communication, and reporting agents collaborate autonomously. Agents are deployed as containerized microservices via Azure Container Apps and communicate through Azure Cosmos DB, enabling shared messaging and state management. A central orchestrator manages task routing, response validation, retries, and cross-agent coordination. Natural-language inputs trigger automated workflows including data processing, email drafting, summarization, and...

Run a multi-agent DevOps loop with the GitHub Copilot app and Agent Framework. Drive parallel agents across isolated worktrees, collaborate in real time on Canvas, build a code-review agent in GitHub Actions, auto-merge on green CI, and validate behavior with ASSERT evals.

Run autonomous local agents safely with OpenClaw and Microsoft Execution Containers. Install OpenClaw and build a multi-step file workflow, sandbox it with MXC policies, automate browser and app control, rebuild in an ephemeral cloud sandbox, and integrate with M365 through Scout.

Manufacturing operations rely on heavy equipment where unplanned downtime causes large financial losses. Maintenance teams often operate reactively due to limited analytics, disconnected logs, and slow fault identification. Manual data collection from IoT systems and equipment logs prevents real-time visibility or predictive planning.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help customer service and technology teams understand how conversational AI can transform customer support operations. The lab guides participants through an end-to-end, production-style solution that builds an intelligent Customer Care Copilot capable of handling customer inquiries, managing complaints, and automating service request escalation. Participants work with real-world customer support scenarios such as order tracking, delivery delays, product returns, and service complaints, using conversational AI to collect relevant details and trigger automated ticket creation. By leveraging Microsoft Copilot Studio and integrating with Freshdesk,...

Ship a production-ready agent on Microsoft Foundry. Set up a Foundry project with the SDK, ground it on a Foundry IQ corpus, build a multi-tool agent with MCP, add guardrails with ACS manifests and prompt shields, and host and monitor it on Foundry Hosted Agents with Azure Monitor.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help HR and technology teams understand how AI can modernize technical recruitment workflows. The lab guides participants through an end-to-end, production-style solution that builds an intelligent HR Interview Copilot capable of evaluating candidate resumes, matching skills against job descriptions, generating role- and experience-specific interview questions, and sharing structured shortlist reports with hiring managers. Participants work with real-world hiring scenarios for roles such as Software Developer and AI Engineer, using conversational AI to analyze resumes, identify strengths and gaps, and recommend shortlisting decisions.

This Hack in a Day learning experience enables participants to build an AI-powered Security Agent that assists security teams in triaging alerts, analyzing security incidents, and recommending response actions. The lab guides participants through building an agent that ingests security alerts, correlates related events, summarizes potential threats, and provides contextual remediation guidance. Participants work with realistic security alert data to simulate real-world SOC workflows, demonstrating how AI agents can reduce alert fatigue and improve response efficiency while keeping humans in control of final actions.

HR struggles to analyze talent profiles, resumes, training records, and performance data distributed across various systems. Identifying skills, matching them to roles, and generating training recommendations requires manual analysis.

This is a comprehensive, enterprise data engineering learning experience designed to help organizations unify disparate data sources and accelerate analytics using Microsoft Fabric. The lab guides participants through building an end-to-end data foundation that ingests structured and unstructured data into the bronze layer, applies transformations in Fabric notebooks for the silver layer, and creates curated business domains in the gold layer. Participants also integrate Azure Databricks for mirrored workloads and enforce governance policies using Microsoft Purview. Finally, they develop Power BI dashboards on top of the gold layer to deliver consistent, executive-ready business insights. The...

Enterprises struggle with fragmented systems across HR, Finance, IT, and Customer Operations. Employees must manually switch between applications and datasets to retrieve insights or initiate actions. This structure increases inefficiency, slows decisions, and prevents collaboration. A unified, AI-driven assistant can bridge these silos to enable natural-language access and automation.

This is a comprehensive, hands-on Hack in a Day learning experience designed to help manufacturing and quality engineering professionals understand how AI can modernize visual inspection workflows. The lab guides participants through an end-to-end, production-style solution that detects surface defects from manufacturing images, classifies defect severity, and generates natural-language inspection commentary. Participants work with real-world defect scenarios such as crazing, patches, and scratches, using computer vision models to identify and score defects with confidence-based predictions. By leveraging Azure AI Custom Vision, Microsoft Foundry, and the GPT-4.1-Mini model, attendees gain practical, real-world insights into building a...