Microsoft’s annual Build conference has once again underscored its ambitions to be at the forefront of AI innovation, rolling out a suite of new database and analytics capabilities aimed squarely at what is rapidly becoming the next wave in business AI: agentic AI development. The timing is critical. As generative AI makes leaps in sophistication, the enterprise world is shifting its focus from passive assistants and copilots to more autonomous, “agentic” systems capable of complex reasoning, orchestration, and workflow automation—integrated tightly with core business data.
Over the course of the last eighteen months, enterprise AI has moved from exploratory pilots—dominated by assistants and copilots performing narrow tasks—to a new paradigm where so-called “AI agents” are emerging as the key to next-generation productivity. These agents don’t just provide answers; they reason over data, automate multi-step processes, and connect data-driven insight directly to actions within business workflows. This pivot follows the watershed moment in late 2022 when OpenAI’s ChatGPT showcased transformative generative AI capabilities, triggering a surge of investment and rapid innovation among cloud and data vendors including AWS, Google Cloud, Databricks, and Snowflake.
This year, Microsoft’s own strategy has crystallized clearly: empower developers to harness agentic AI through integrated, scalable platforms—starting with its AI-powered Fabric suite and extending across its increasingly intelligent application ecosystem.
This is more than a technical detail: semistructured data often contains operational signals, logs, customer interactions, or IoT data that traditional relational databases don’t capture well. By making this data available natively within Fabric—and tightly integrating with the Azure AI Foundry development environment—Microsoft is empowering developers to feed richer, real-time context into their AI applications and analytics. Analysts such as Stephen Catanzano emphasize the significance: “Cosmos DB in Fabric is a big addition… enabling organizations to incorporate semistructured operational data alongside their structured [data] and providing developers flexibility to build modern AI applications with just a few clicks.”
Michael Ni of Constellation Research frames this evolution aptly: “By embedding agents into Power BI, Microsoft isn’t just adding another conversational BI feature—it’s activating a decision layer.” In effect, Power BI is transforming from a visualization endpoint into a conversational interface for enterprise decision-making. The chat capabilities, set for general availability in the coming months, directly address a long-standing bottleneck: surfacing actionable insights for business users amid sprawling datasets and complex semantic layers.
Frank Shaw, Chief Communications Officer at Microsoft, articulated this at Build: “Our goal is to help developers turn their ideas into reality.” CTO Kevin Scott highlighted Microsoft’s philosophy of providing breadth and depth: “…we’re going to give you a ton of tools to work with, and we hope you do something with them.”
This is more than rhetoric. As highlighted by Michael Ni, the platform’s modular and extensible foundation makes it possible for developers to fuse real-time data from Cosmos DB, orchestrate workflows with Fabric, and surface analytics through Power BI’s conversational interfaces—enabling low-friction, large-scale decision-making across the Microsoft ecosystem.
However, Microsoft’s greatest advantage may be its ability to collapse the boundaries between data storage, analytics, workflow, and frontend conversational experiences, offering developers and enterprises a “one-stop platform” for intelligent application development. If successful, Fabric could become the de facto substrate for agent-driven transformation in the Microsoft-centric enterprise landscape.
Yet, those embracing Microsoft’s agentic AI capabilities must proceed thoughtfully. The opportunities—accelerated insight, automation, and business agility—are compelling, but so too are the risks: complexity, data stewardship, and potential overreliance on a single technology vendor.
Ultimately, as agents become the new backbone of digital business, success will hinge not just on platform features, but on critical thinking, governance, and a steady eye on both innovation and responsibility. For organizations poised to leverage Microsoft’s evolving AI suite, now is the time to skill up, experiment, and strategically plan for an agent-powered future—one where the boundary between data, decision, and action may all but disappear.
Source: TechTarget New Microsoft database, analytics tools target agentic AI | TechTarget
The Rise of Agentic AI and Microsoft’s Strategic Response
Over the course of the last eighteen months, enterprise AI has moved from exploratory pilots—dominated by assistants and copilots performing narrow tasks—to a new paradigm where so-called “AI agents” are emerging as the key to next-generation productivity. These agents don’t just provide answers; they reason over data, automate multi-step processes, and connect data-driven insight directly to actions within business workflows. This pivot follows the watershed moment in late 2022 when OpenAI’s ChatGPT showcased transformative generative AI capabilities, triggering a surge of investment and rapid innovation among cloud and data vendors including AWS, Google Cloud, Databricks, and Snowflake.This year, Microsoft’s own strategy has crystallized clearly: empower developers to harness agentic AI through integrated, scalable platforms—starting with its AI-powered Fabric suite and extending across its increasingly intelligent application ecosystem.
Azure Cosmos DB in Fabric: Expanding the Canvas for AI-Ready Data
A centerpiece of the latest announcements is the deep integration of Azure Cosmos DB—Microsoft’s enterprise-grade, NoSQL database—into Fabric, its end-to-end data management and analytics platform. Now in preview, Cosmos DB in Fabric allows organizations to seamlessly bring both structured and semistructured data (for example, JSON files and other document formats) into unified analytics and AI workflows.This is more than a technical detail: semistructured data often contains operational signals, logs, customer interactions, or IoT data that traditional relational databases don’t capture well. By making this data available natively within Fabric—and tightly integrating with the Azure AI Foundry development environment—Microsoft is empowering developers to feed richer, real-time context into their AI applications and analytics. Analysts such as Stephen Catanzano emphasize the significance: “Cosmos DB in Fabric is a big addition… enabling organizations to incorporate semistructured operational data alongside their structured [data] and providing developers flexibility to build modern AI applications with just a few clicks.”
Power BI’s New Chat with Data: From Dashboards to Decision Layers
Data democratization has always been a promise of self-service analytics, but Power BI’s forthcoming “Chat with your Data” feature signals a paradigm shift. Moving beyond the ability to ask natural language questions about a single report, users will soon be able to issue queries spanning multiple reports, semantic models, and applications—interacting with AI-powered Fabric agents for deeper, cross-system analysis.Michael Ni of Constellation Research frames this evolution aptly: “By embedding agents into Power BI, Microsoft isn’t just adding another conversational BI feature—it’s activating a decision layer.” In effect, Power BI is transforming from a visualization endpoint into a conversational interface for enterprise decision-making. The chat capabilities, set for general availability in the coming months, directly address a long-standing bottleneck: surfacing actionable insights for business users amid sprawling datasets and complex semantic layers.
Building a Developer Ecosystem for Agentic AI
Beyond high-profile features like Cosmos DB and Power BI chat, Microsoft’s Build announcements include several under-the-hood upgrades designed to enable agentic AI development at scale:- Custom Fabric Data Agents: Developers using Copilot Studio can now augment custom agents with prebuilt Fabric data agents, capable of leveraging data in OneLake—Fabric’s foundational data lake—for building more context-aware, actionable AI experiences. These agents can automate processes, trigger actions, and discover insights within Microsoft Teams or 365 Copilot channels.
- Azure AI Foundry Integration: By aligning with Azure Databricks and its robust data engineering capabilities, Azure AI Foundry empowers developers to create sophisticated agents leveraging both operational and analytical data—a critical requirement for agentic AI.
- Digital Twin Builder: Fabric’s new digital twin builder simplifies the development of digital twins for scenario planning and simulation—vital for industries like manufacturing, supply chain, and urban planning where understanding complex system dynamics is essential.
- PostgreSQL Integration in GitHub Copilot: Microsoft is extending support for PostgreSQL, a popular open-source database, within GitHub Copilot, accelerating the development of GenAI-powered applications for developers using modern data stacks.
- SQL Server 2025 Updates: The newest version of SQL Server offers streamlined integrations with both Azure and Fabric, bringing established relational database tools closer to Microsoft’s AI and analytics ecosystem.
- Data Transformations in OneLake: New options for “shortcut transformations” let users automatically convert ingested data to the Delta Lake format or trigger AI-powered transformations such as document classification, smoothing the path from raw data to analytics-ready format.
- Translytical Capabilities in Power BI: Preview features in Power BI now enable users to automate in-report actions, further closing the loop between data discovery and operational execution.
Integrated, Not Piecemeal: The Fabric Philosophy
A recurring theme from Microsoft executives and independent analysts is that these new features are not simply standalone tools, but part of a “runtime for decisions, AI agents, and intelligent action.” The intention is for Fabric to serve as the unified substrate—removing barriers between data, insight, and action. By tightly coupling data lakes, analytics, conversational interfaces, and developer tools, Microsoft is providing the scaffolding for organizations to build intelligent, agent-driven applications from inception—and do so at scale.Frank Shaw, Chief Communications Officer at Microsoft, articulated this at Build: “Our goal is to help developers turn their ideas into reality.” CTO Kevin Scott highlighted Microsoft’s philosophy of providing breadth and depth: “…we’re going to give you a ton of tools to work with, and we hope you do something with them.”
This is more than rhetoric. As highlighted by Michael Ni, the platform’s modular and extensible foundation makes it possible for developers to fuse real-time data from Cosmos DB, orchestrate workflows with Fabric, and surface analytics through Power BI’s conversational interfaces—enabling low-friction, large-scale decision-making across the Microsoft ecosystem.
Strengths and Strategic Advantages
1. Unified Data and AI Stack for Enterprise Developers
By deeply integrating NoSQL, SQL, AI, and analytics within Fabric—and extending those to Power BI, Teams, Copilot, and beyond—Microsoft has created a tightly-knit data platform rarely seen in enterprise technology. This unity enables:- Faster time-to-insight: With real-time data in Cosmos DB and analytical capabilities in Power BI and Fabric, developers and business users can iterate faster, with less data movement and wrangling.
- Consistent semantic and security models: Fabric’s approach reduces the fragmentation seen when piecing together disparate clouds and third-party tools.
- AI-native data experiences: Embedding natural language interactions and agentic automation at every layer helps drive adoption and value realization without steep learning curves.
2. Flexible Data Model Support
By supporting both structured and semistructured data—including JSON and IoT signals—Microsoft acknowledges that enterprise data is messy and ever-expanding. This flexibility paves the way for more robust AI models, which rely on context-rich, heterogeneous data sources.3. Developer-Focused Extensibility
The enhancements to agent integration in Copilot Studio, deep Databricks compatibility, and the inclusion of open-source databases like PostgreSQL signal Microsoft’s clear intent to embrace—and empower—the broader developer community, not just those steeped in Microsoft-native stacks.4. Broad Ecosystem Reach
By integrating new capabilities into Power BI and Teams, Microsoft positions its agentic AI tools squarely where business users already work, facilitating mass adoption. The strategy promises to make intelligent, agent-driven insights accessible across reporting, collaboration, and workflow channels.Risks, Limitations, and Open Questions
Despite the clear strengths, several important risks and challenges remain as Microsoft’s agentic AI platform matures.1. Complexity and Learning Curve
While unified integration is a virtue, the growing breadth of capabilities within Fabric and its connected services also raises the barrier for organizations without mature data engineering or AI expertise. Orchestrating seamless pipelines from operational data to actionable agentic insights will require investment in skills development and possibly, external support.2. Data Security and Governance
As organizations bring ever more sensitive—and semistructured—data into unified platforms like Fabric, robust data governance, security controls, and auditability become crucial. Microsoft’s track record with data governance is generally strong, but the addition of conversational interfaces and agentic automation increases the risk of unintended data exposure, especially if access policies are not rigorously enforced.3. Vendor Lock-In
Microsoft’s cohesive data and AI ecosystem offers undeniable efficiencies, but it may also increase dependency on Microsoft-specific tooling and cloud services. While integrations with Databricks, GitHub, and PostgreSQL help lessen this risk, organizations should carefully assess how deeply core workflows become tied to Microsoft’s proprietary data models and APIs.4. Early Days for Agentic AI
The concept of agentic AI in production is still nascent for many enterprises. Success stories are mostly in development rather than broad deployment, and best practices for AI agent design, monitoring, and ethical governance are still evolving. Organizations should be cautious of overpromising agent capabilities, especially in regulated industries.5. Performance and Scalability at Scale
The technical specs and early previews show promise, but real-world workloads—especially those involving multimodal data, real-time streams, or high concurrency—will put Microsoft’s new features to the test. Customers should closely monitor performance benchmarks, SLAs, and general availability timelines as features move from preview into production.Competitive Landscape and Market Implications
Microsoft isn’t alone in targeting the agentic AI revolution. AWS is investing heavily in SageMaker Agents and Bedrock, while Google Cloud’s Vertex AI offers its own extensions for agent management and orchestration. Databricks and Snowflake are also racing to support agentic workflows atop their unified data platforms.However, Microsoft’s greatest advantage may be its ability to collapse the boundaries between data storage, analytics, workflow, and frontend conversational experiences, offering developers and enterprises a “one-stop platform” for intelligent application development. If successful, Fabric could become the de facto substrate for agent-driven transformation in the Microsoft-centric enterprise landscape.
Conclusion: The Road Ahead for Microsoft, Developers, and Enterprise AI
The newly unveiled Fabric and database features point unmistakably to Microsoft’s vision for the next decade of enterprise AI—a vision rooted in seamless data access, agentic orchestration, and actionable insight at scale. The announcements at Build showcase tangible progress, particularly in unifying data formats, embedding AI agents, and lowering barriers to building powerful, context-aware applications.Yet, those embracing Microsoft’s agentic AI capabilities must proceed thoughtfully. The opportunities—accelerated insight, automation, and business agility—are compelling, but so too are the risks: complexity, data stewardship, and potential overreliance on a single technology vendor.
Ultimately, as agents become the new backbone of digital business, success will hinge not just on platform features, but on critical thinking, governance, and a steady eye on both innovation and responsibility. For organizations poised to leverage Microsoft’s evolving AI suite, now is the time to skill up, experiment, and strategically plan for an agent-powered future—one where the boundary between data, decision, and action may all but disappear.
Source: TechTarget New Microsoft database, analytics tools target agentic AI | TechTarget
