Content
Day 2 of Microsoft Build 2026 was where the conference shifted from announcing the future to showing how to build it. If Day 1 was Satya Nadella drawing the map — agents as a new execution layer, Foundry as the platform, GitHub Copilot as the development control centre, Agent 365 as the governance layer — then Day 2 was the hands-on work of following the directions.
We followed the second day online, watching the session stream and tracking the community reaction in real time. Two things stood out immediately: the data story came into much sharper focus with the arrival of Project Rayfin, and the production story for AI agents got substantially more concrete. Both matter a great deal for the enterprise teams we work with.
Setting the Tone – Scott Guthrie on What Actually Matters
Day 2 opened with a short fireside chat from Scott Guthrie, Microsoft’s Executive VP of Cloud and AI. The format was tight — 15 minutes, builder’s perspective, no slides. Guthrie’s framing was useful precisely because it was unsentimental: there is a great deal of noise about AI, and most of it is not useful to developers and organisations who need to make decisions now.
His three-part framework for Day 2 — AI-ready infrastructure, context layers for agents, and AI-assisted modernisation — turned out to be an accurate map of what the day’s sessions actually delivered. It was a quiet but deliberate signal: Microsoft is no longer selling the vision of agentic AI. It is selling the path from where your organisation is today to where agents can operate productively.
Project Rayfin – Apps Are Coming to the Data Platform
Of everything announced across both days of Build 2026, Project Rayfin generated the strongest reaction from the Microsoft community — and for good reason. It represents a genuine architectural shift in how applications interact with data, one that will be directly relevant to any team building on Microsoft Fabric.
The core idea is simple to state but significant in its implications: with Rayfin, your entire application backend is expressed in code, and Rayfin translates that code into the services it needs — databases, functions, processing pipelines and more — running natively on Fabric. You write the logic. Rayfin handles the plumbing.
And because it runs inside Fabric, your application has native access to all of Fabric’s data, analytics and AI capabilities without stitching together separate backend services.
The BRK225 breakout took this further. The phrase that stood out was: “Apps are coming to the data platform.” That is a meaningful inversion of the traditional pattern, where data moves to wherever the application runs.
With Rayfin, the application runs where the data lives. For teams that have spent years managing ETL pipelines, data movement costs and synchronisation complexity, this is the kind of shift that rewrites assumptions about what good architecture looks like.
Agents are a first-class part of the Rayfin story. You can describe the application you want to build in natural language and let agents generate the initial backend for you, or write it directly in code. Either path deploys via CLI into Fabric as a secure, scalable solution. The vision is that AI-assisted development and data-native applications are not separate concerns — they converge on the same platform.
The Microsoft Power BI and Fabric community’s reaction was immediate and emphatic. One Microsoft MVP in the r/MicrosoftFabric community called Rayfin “the best announcement since the release of Power BI Desktop” — high praise in a community that has seen a great deal of innovation since Fabric launched.
For Multishoring’s clients working with Power BI, Microsoft Fabric and data integration, Rayfin is the session we will be discussing most in the coming months. It does not change everything overnight, but it does change the direction of travel in a way that affects architectural decisions being made right now.
The Intelligence Layer – Making Agents Context-Aware by Design
One of the structural weaknesses of early enterprise AI deployments has been context. Agents can be technically impressive while remaining practically unreliable if they cannot access the right business knowledge, respect the right permissions and understand the right organisational relationships. This is the problem that BRK240 addressed directly, and it is arguably the most important session of Build 2026 for enterprise architects.
Microsoft’s answer is a three-layer intelligence architecture built into the agent platform:
- Foundry IQ — reusable, permission-aware knowledge bases for agentic retrieval. Agents can access enterprise knowledge without every team rebuilding its own retrieval pipeline from scratch.
- Fabric IQ — structured business context: semantic models, ontologies, KPIs, relationships and governed analytics. Agents can reason over business data, not just raw data, because the meaning has been preserved and shared.
- Work IQ — organisational signals from Microsoft 365: documents, meetings, emails, workflows and the relationships between people and projects. Agents can operate with awareness of how work is actually structured in your organisation.
The practical implication is substantial. Generic agents struggle because they lack the shared meaning that humans take for granted: what ‘customer’ means in your system, how forecasting works in your organisation, which approvals are required for which actions. The three IQs are Microsoft’s attempt to make that shared meaning available to agents in a governed, reusable way.
For teams that have already invested in data governance and semantic modelling — including Power BI semantic layers, Fabric lakehouses and structured data assets — this architecture directly rewards that investment. The better your data foundations, the more capable your agents will be. The message was clear: agent quality is a downstream consequence of data quality.
Agents Don’t Fail in Demos — They Fail in Production
BRK231 opened with one of the most honest sentences of the entire conference. It landed because it is true, and because it is the gap that most enterprise teams struggling with AI adoption are currently sitting inside.
The session covered how teams can use fine-tuning and reinforcement learning on Microsoft Foundry to improve production agents using real usage signals. This is an important shift from the typical AI development pattern. Instead of treating a deployed agent as a finished product, the session framed deployment as the beginning of an improvement cycle: the agent runs in production, generates signals about where it succeeds and where it falls short, and those signals feed back into fine-tuning and RL runs that make the next version better.
The session also addressed a practical question that development teams face constantly: when do you use fine-tuning versus reinforcement learning? The short version: fine-tuning is effective for reducing cost and latency when the task pattern is well-understood; RL delivers deeper gains when the agent needs to improve on complex, multi-step behaviour. Foundry is designed to make both options accessible without requiring a specialised ML team.
The observability angle matters here too. You cannot improve what you cannot observe. A companion session (BRK252) went deeper on this: nondeterministic, multi-agent systems break traditional monitoring approaches.
The new requirement is cross-framework tracing, context-specific evaluations and always-on signals that connect agent behaviour to business outcomes and cost. The message was consistent with the broader Day 2 theme: production AI requires the same operational discipline as production software.
Our Microsoft Services You Might Find Interesting
Multishoring’s Build 2026 Verdict
We followed Build 2026 across both days with one question in mind: what does this mean for the teams we work with? After two days of keynotes, breakouts and community reaction, our answer splits into two distinct messages depending on where you sit.
For data and analytics teams – Rayfin is the story of 2026
Project Rayfin is not a minor product update. It is a fundamental rethink of the relationship between applications and data. If your team is building on Microsoft Fabric, or planning to, the architecture question is no longer “how do we move data to the application?” It is “how do we build the application where the data already lives?”
Combined with Fabric IQ’s semantic layer, the three-IQ intelligence architecture and Azure HorizonDB’s AI-native PostgreSQL capabilities, Microsoft is assembling a data platform that is designed from the ground up for the agent era. Teams that have been doing the hard foundational work — clean data models, governed semantic layers, structured Fabric assets — are now positioned to build on top of something genuinely powerful.
The Power BI and Fabric community’s reaction to Rayfin was not accidental. It reflects recognition that something architecturally significant has arrived. We share that view, and we will be helping clients think through what it means for their Fabric roadmaps over the coming months.
For IT leaders and enterprise architects – the agent stack is production-ready
Build 2026 answered the question that Build 2025 raised. Last year, Microsoft announced the components of an agentic AI stack. This year, those components were shown working together in production scenarios, with governance, observability, fine-tuning and security built into the same platform layer.
Agent 365 gives IT teams the control plane they need to manage agents as managed digital workers rather than uncontrolled automations. Foundry’s RL and fine-tuning capabilities mean agents can be improved using real usage data. The three-IQ layer means agents can be grounded in enterprise context rather than operating on generic knowledge. MXC and Windows 365 for Agents mean local and cloud execution can be governed at the OS level.
This is not a complete solution to every enterprise AI challenge. But it is a coherent, end-to-end architecture with a clear path from proof-of-concept to governed production. The organisations that start building on it now will have a meaningful head start on those that wait for the next Build announcement.
“Build 2026 was the year Microsoft closed the gap between announcing agentic AI and showing how to ship it. The platform is not finished — no enterprise platform ever is — but it is now real enough to build on. The Rayfin announcement, the intelligence layer, the production observability work and the governance story together represent the most coherent vision Microsoft has presented for enterprise AI. We leave Build 2026 with a clearer roadmap for our clients than we arrived with.”
Three Things Enterprise Teams Should Do Next
- Evaluate your Fabric foundations for the Rayfin era. Rayfin works best when your data platform already has clean semantic models, governed data assets and clear ownership. If your Fabric implementation is still fragmented or poorly governed, now is the right time to address it — before building applications and agents on top of it. Talk to us about a data maturity assessment.
- Define your agent governance model before you have agents in production. Agent 365, MXC, the three-IQ layer and Foundry’s observability tools all assume you have thought about identity, permissions, audit requirements and human-in-the-loop boundaries. Retrofitting governance after deployment is significantly harder. Build 2026 provided the vocabulary and the platform. Now is the time to build the policy framework.
- Start a Foundry proof-of-concept with your own data. The difference between an enterprise team that understands agentic AI in theory and one that understands it in practice is a working prototype connected to real business data. The toolchain — Foundry, GitHub Copilot, Fabric IQ, the agent service — is mature enough to build on. The earlier you start, the more you learn, and the more useful your feedback will be as the platform continues to evolve.
