Tag Archives: copilot

Fabric IQ + Foundry IQ: Building the Unified Intelligence Layer for Agentic Apps

Core Components of the IQ Layer

Fabric IQ adds business logic to OneLake data with Maps, Graphs, and Digital Twins, enabling spatial and relational analysis. Foundry IQ powers agentic retrieval via Azure AI Search, automating RAG pipelines for multimodal data while enforcing Purview governance.

Work IQ integrates Microsoft 365 signals like Teams conversations, creating a “one brain” for agents that blends quantitative Fabric data with qualitative context—no more hallucinations from poor grounding.

Real-World Manufacturing Example

A manufacturer models factory disruptions in Fabric IQ Graphs. Foundry IQ agents prompt: “Analyze Line 3 downtime ripple effects on orders.” The system queries live streams, predicts delays, and auto-alerts via Teams, cutting response times 70%.

Retail Digital Twin in Action

Retailers use Fabric IQ Digital Twins for store IoT data. Foundry agents optimize: “Adjust shelf stock by foot traffic and sales.” Results include visuals, forecasts, and auto-reorders, lifting margins 15% with zero custom code.

Getting Started Roadmap

Enable in F64+ capacities, link via Data Agents, pilot sales/ops queries. Track insight velocity to justify scale-up.

#MicrosoftFabric #AzureAIFoundry #FabricIQ #AgenticAI

5 Practical Use Cases: Fabric Data Agents Powering Foundry HR and Sales Copilots

Fabric Data Agents bridge natural language to enterprise data, fueling Foundry copilots for HR and sales teams with secure, real-time insights.

These agents auto-generate SQL, KQL, or DAX over OneLake, letting non-technical users query without IT—perfect for high-velocity business decisions.

HR Copilot: Staffing Insights

HR prompts Foundry: “Show staffing gaps by role and region.” Data Agent scans Fabric warehouses, returns trends with turnover risks, embedded in Teams for instant action—slashing recruitment delays 40%.

Sales Performance Copilot

Sales managers ask: “Top lost deal reasons with revenue impact.” Agent pulls Fabric lakehouse transactions, generates infographics, and suggests upsell targets—boosting close rates 25%.

Productivity Analytics

“Analyze team output vs. benchmarks.” Combines Fabric metrics with 365 signals via Foundry IQ, spotting burnout patterns for proactive interventions.

Compliance Queries

“Flag policy violations in Q4 hires.” Grounds responses in Purview-governed data for audit-ready reports.

Deployment Tips

Publish agents from lakehouses/warehouses, connect in Foundry projects. Start with 5-10 queries, measure time savings.

#MicrosoftFabric #DataAgents #Copilots #HRTech

Azure AI Foundry and Microsoft Fabric: Driving Data Unification and the Agentic World

The Power of Data Unification

Fabric consolidates lakehouses, warehouses, pipelines, and real-time streams into OneLake, eliminating data movement and enabling governance at scale with Purview lineage.

Foundry builds on this by connecting agents to Fabric Data Agents—endpoints that translate natural language to SQL, KQL, or Spark code—grounding responses in governed datasets for hallucination-free insights.

Developers get SDKs, notebooks, and MLOps for full lifecycles, while business users prompt agents in Teams or apps for instant analytics, accelerating from PoC to production.

Case Study 1: Gay Lea Foods Accelerates Reporting with Fabric

Canadian dairy co-op Gay Lea Foods struggled with slow, manual reporting across supply chain data. They unified 100TB of operational data in Fabric lakehouses and warehouses, cutting report generation from days to minutes.

Real-Time Intelligence processes live inventory streams; Power BI visuals embed in Teams for plant managers. Adding Foundry agents, ops teams now ask “Predict milk production shortfalls by farm,” blending Fabric queries with predictive reasoning for 30% faster decisions.

Results: Reporting time slashed 80%, supply chain efficiency up 25%, with full audit trails for compliance—all on F64 capacity with auto-scaling.

Case Study 2: Global Retailer Masters Demand Forecasting

A major retailer faced siloed POS, e-commerce, and supplier data, leading to stockouts during peaks. Fabric pipelines ingest petabyte-scale streams into OneLake, with Spark jobs running ML baselines on lakehouses.

Foundry agents link via Data Agents: “Forecast holiday demand by SKU, factoring weather and promotions.” Agents orchestrate KQL on eventhouses, SQL on warehouses, and return visuals with confidence scores embedded in Dynamics 365.

Impact: Forecast accuracy improved 35%, inventory costs down 22%, and non-technical buyers access insights via chat—scaling to 500 stores without added headcount.

Key Capabilities Fueling the Agentic Shift

OneLake acts as the semantic layer, with shortcuts to external sources like Snowflake or S3, feeding Foundry’s 1400+ connectors for hybrid data unification.

Agentic workflows shine: Foundry IQ evaluates responses against Fabric ground truth; multi-agent systems divide tasks like “Query sales data, then optimize pricing via ML.” Copilot accelerates Fabric notebooks 50% for prep work.

Gartner’s 2025 Leaders status confirms this—Microsoft tops vision/execution for AI apps and data integration, powering 28K Fabric customers with 60% YoY growth.

Security layers include passthrough auth, RBAC, encryption at rest/transit, and Purview for lineage, making it enterprise-ready for regulated sectors.

Why This Drives the Agentic World

Enterprises shift from dashboards to agents because unified data + orchestration = reliable AI at scale. Fabric handles volume/variety; Foundry adds reasoning/tools for outcomes like auto-remediation or cross-system actions.

Customers see 40-60% dev savings, 25%+ prediction gains, and seamless Teams/Power App embedding—unlocking ROI where legacy BI falls short.

Roadmap and Strategic Advice

Microsoft roadmap deepens integration: Global fine-tuning in Foundry, adaptive Fabric capacities, and edge agents via Azure Arc for IIoT unification.

Data leaders: Pilot Fabric on top workloads, expose Data Agents for 5-10 queries, then deploy Foundry pilots in sales/ops. Measure time-to-insight and scale via reservations.

This duo doesn’t just unify data—it builds the agentic world where AI acts on your estate autonomously.

#MicrosoftFabric #AzureAIFoundry #DataUnification #AgenticAI #GartnerLeader

Azure AI Foundry: The Enterprise AI Control Plane You’ve Been Waiting For

What Azure AI Foundry Is

Azure AI Foundry (now branded simply as Microsoft Foundry) is a unified environment to design, build, evaluate, and operate AI applications and agents at scale. It brings together model catalog, orchestration, security, governance, and MLOps in a single, enterprise-ready experience.

It provides access to a broad catalog of foundation models, including OpenAI GPT, Anthropic Claude, and other third-party or open-source models under one roof.
Teams can collaborate in projects that bundle datasets, prompts, tools, agents, and deployment assets with built-in lifecycle management.

Key Capabilities That Matter

Under the hood, Azure AI Foundry is much more than a model playground; it is an opinionated platform for building production workloads.

Unified development experience: SDKs, CLI, and a portal provide consistent workflows with versioning, reusable components, and integrated notebooks for end-to-end AI development.
Agentic experiences: Foundry Agent Service enables multi-agent orchestration, tool usage via Model Context Protocol, and deep integration into Microsoft 365 and business systems.
Native MLOps: Built-in pipelines support training, evaluation, deployment, and monitoring of models with CI/CD via GitHub and Azure DevOps.

Governance, Security, and Responsible AI

For enterprises, AI is only real when it is secure, governed, and compliant. Azure AI Foundry leans heavily into these requirements.

Enterprise governance: Role-based access control, audit trails, and project-level isolation help segment workloads and protect sensitive assets.
Data control: Organizations can bring their own storage and Key Vault, ensuring data residency, encryption, and retention align with internal policies.
Risk and safety tooling: Content filtering, policy configurations, and evaluation workflows support responsible AI practices across models and scenarios.

Architecting Real-World Use Cases

The real power of Foundry shows up when it is applied to concrete business problems.

RAG and knowledge agents: Foundry makes it straightforward to build Retrieval-Augmented Generation experiences over secured enterprise data, reducing the need for heavy fine-tuning.
Line-of-business copilots: With connectors into Microsoft 365, Dynamics, and hundreds of SaaS systems, you can design agents that work across email, documents, CRM, and operations data.
Edge and hybrid scenarios: Support for cloud, on-premises, and edge deployment enables predictive maintenance, IoT analytics, and offline/low-connectivity use cases.

Strategic Guidance for Data & AI Leaders

For architects and data leaders, Azure AI Foundry is not just another service; it is a strategic control plane for enterprise AI.

Treat Foundry as the standard entry point for generative AI, with central governance over models, prompts, tools, and data connections.
Align AI projects with existing data platforms (Fabric, Synapse, lakehouses) and security baselines, so Foundry becomes an extension of your broader data and cloud strategy—not a silo.
Start with high-impact, low-friction scenarios—knowledge copilots, developer productivity, and customer service—and then scale into multi-agent, cross-domain workflows as maturity increases.

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Deepak Kaushik -> Cloud Blooded Beast

Azure Nerd, Azure SQL DWH, Azure Storage, POWER BI, AI