Gain an early view of the Microsoft priorities shaping FY27
As AI innovation, agentic applications, and Microsoft Marketplace opportunities continue to reshape customer demand, early alignment has never been more important. This Microsoft Marketplace community article explores why MCAPS Start for Partners on July 22 is a key moment for Software Development Company (SDC) partners, ISVs, and Marketplace publishers looking to align with Microsoft’s FY27 priorities. Learn how Microsoft Marketplace investments, co-sell opportunities, app modernization initiatives, and emerging AI trends can influence your growth strategy, solution roadmap, and customer engagement plans for the year ahead. The article also highlights practical steps partners can take now to strengthen Marketplace readiness, identify areas for differentiation, and translate Microsoft priorities into actionable business outcomes. If your organization develops, sells, or publishes solutions through Microsoft Marketplace, this timely read will help you prepare for the opportunities shaping FY27. 👉 Read the full article: Why MCAPS Start for Partners on July 22 Matters for Software Development CompaniesWhy MCAPS Start for Partners on July 22 matters for software development companies
Every new fiscal year begins with questions. Where is customer demand heading? Which technology investments matter most? How can software companies build solutions that stand out in an increasingly competitive Microsoft ecosystem? Software development companies, Marketplace publishers, architects, and developers across our partner community express a common theme: everyone is looking for clarity. Customers are moving quickly from AI experimentation toward operational AI adoption. New opportunities are emerging around agentic applications, modernization, and Microsoft Marketplace growth. At the same time, partners want to know where Microsoft is investing and how they can align their business for success. That is exactly why MCAPS Start for Partners on July 22 matters. This event is designed to help partners start FY27 with a clear understanding of Microsoft priorities, customer demand, and the opportunities ahead. More importantly, it is an opportunity to align early, before the year gains momentum and before critical decisions are made across solution development, go-to-market planning, and Marketplace strategy. The conversation we keep hearing from partners Over the last year, many software companies have invested heavily in AI. Teams have built proofs of concept, launched copilots, explored automation, and experimented with agents. Those efforts have created valuable learning, but customers are now asking a different set of questions. Instead of asking what AI can do, customers are asking how AI can create measurable business outcomes. They want solutions that can be governed, operationalized, and scaled across teams and business units. They want trusted platforms that protect data and intellectual property while helping them move faster. This shift represents something larger than a technology trend. It reflects what Microsoft describes as Frontier Transformation: the movement from isolated AI experimentation toward AI and agents becoming part of repeatable business operations. For software companies and Marketplace publishers, that creates a significant opportunity. The partners who can help customers operationalize AI, modernize applications, and deliver scalable outcomes will be positioned to lead customer conversations in FY27. MCPS for Partners provides an early look into how Microsoft is thinking about this transformation and where partners can create value. Why early alignment matters One lesson learned from working with software partners is that timing matters. The strongest partners rarely wait until priorities become widely understood. They start early. They align their product roadmap, Marketplace strategy, sales motions, and technical investments before demand peaks. MCAPS Start for Partners is designed to provide that early alignment. During the event, Microsoft leaders will share perspectives on FY27 priorities, innovation areas, investment focus, and growth opportunities. Partners will gain visibility into topics including AI innovation, agentic applications, Marketplace growth, co-sell opportunities, security, cloud platforms, and business applications. For developers and technical leaders, this means understanding where customers are investing and how emerging capabilities may influence future solution architectures. For Marketplace publishers, it means learning where discoverability, go-to-market alignment, and Marketplace motions can create growth opportunities. For business leaders, it means connecting Microsoft investments to practical execution plans that support long-term growth. The Marketplace opportunity is becoming more strategic Customers increasingly want streamlined procurement experiences and trusted solution sources. At the same time, partners are looking for scalable ways to expand reach and accelerate customer acquisition. That makes Marketplace more than a publishing destination. It becomes a strategic go-to-market channel. Organizations attending MCAPS Start for Partners will hear how Marketplace fits into broader partner growth strategies and how Marketplace motions can support customer demand generation and co-sell engagement. For software companies, this is an opportunity to think beyond listings and consider how Marketplace can support solution differentiation, visibility, and customer engagement throughout FY27. The most successful Marketplace publishers are not simply reacting to market changes. They are aligning their offerings to where customer demand is moving and where Microsoft is investing. Preparing for the conversations that will shape FY27 As July 22 approaches, partners can consider customer conversations happening today. Where are customers requesting guidance around AI transformation? Which application modernization projects are gaining momentum? How are security, governance, and scalability influencing solution requirements? These questions are increasingly connected. Customers are seeking partners that can help them connect AI, cloud, security, and business outcomes into a cohesive strategy. They are looking for expertise that goes beyond implementation and extends into long-term transformation. MCAPS Start for Partners was designed to help organizations think through those challenges and identify where Microsoft priorities intersect with customer demand. That alignment becomes even more valuable when technical leaders, sales teams, Marketplace owners, marketing teams, and delivery organizations participate together. When everyone hears the same guidance and leaves with shared priorities, execution becomes significantly easier. What attendees should listen for As you participate in the event, consider listening through three lenses: Pay attention to where customer demand is moving. Understanding demand signals can help guide product investments, roadmap decisions, and solution packaging. Look for insights into Microsoft investments and priorities. These insights can help partners determine where to focus skilling, innovation, and go-to-market efforts. Think about differentiation. Customers increasingly want partners who can move beyond experimentation and deliver repeatable outcomes. The organizations that combine technical excellence with operational execution will have a distinct advantage. The goal is not simply to collect information. The goal is to leave the event with clarity about what actions matter most for your organization. Turning insight into action Every fiscal year presents new opportunities, but not every partner starts with the same level of insight. MCAPS Start for Partners provides software companies, developers, architects, and Marketplace publishers with an early opportunity to understand Microsoft’s FY27 priorities, evaluate evolving customer demand, and identify where AI innovation, agentic applications, Marketplace growth, and co-sell opportunities are creating new possibilities. The partners that act early will be better positioned to build differentiated offerings, strengthen customer conversations, and execute with confidence throughout the year. Registration closes soon, and July 22 is quickly approaching. If you have not secured your place, now is the time to act. Register today, invite your cross-functional team, and ensure your organization is prepared for the Microsoft priorities, investments, and customer opportunities that will define FY27. Whether you are responsible for product strategy, solution architecture, Marketplace growth, sales execution, or customer success, MCAPS Start for Partners offers valuable insights that can help transform planning into action. 👉 Register for MCAPS Start for Partners Before July 22Grow your business globally while selling locally through Microsoft Marketplace
Ready to reach new markets and grow through trusted partner ecosystems? Microsoft is expanding multiparty private offers to Australia, Japan, and South Africa, creating new opportunities for software companies and channel partners to scale globally while selling locally. Discover how Microsoft Marketplace helps partners accelerate co-sell motions, simplify procurement, close larger deals, and unlock new revenue opportunities through partner-led selling. Read the full article: Drive local growth with Microsoft MarketplaceDiscover how Microsoft Marketplace helps accelerate AI innovation
Looking to simplify the path from AI exploration to commercial success? This Microsoft Marketplace event highlights how software development companies can access AI models, streamline procurement and deployment, integrate developer solutions, and scale customer reach through a single trusted platform. Learn practical strategies for packaging, monetizing, and distributing AI-powered solutions across Azure, Microsoft 365, and the broader Microsoft partner ecosystem. Whether you're focused on product development, go-to-market strategy, alliances, procurement, or IT operations, this session offers valuable insights into accelerating time to market and delivering customer value through Microsoft Marketplace. Learn more: https://techcommunity.microsoft.com/event/azureevents/from-ai-evaluation-to-deployment-with-microsoft-marketplace/4537062Dragon Copilot and Microsoft Marketplace are transforming the way healthcare is delivered
If AI has so much promise in healthcare, why does it still feel so hard to apply in everyday workflows? That question is starting to shape much of the conversation across the industry. Healthcare teams aren’t debating whether AI matters anymore, they’re focused on how to make it work in environments that are already stretched thin. Reality: Healthcare has a capacity problem Healthcare isn’t dealing with a demand problem; it’s dealing with a capacity constraint. In fact, 79% of healthcare workers say they don’t have enough time or energy to do their work, 51% of healthcare leaders say productivity needs to increase, and 79% are confident AI will play a role in expanding organizational capacity. That pressure shows up everywhere: in documentation backlogs, fragmented and click-heavy workflows, administrative overload, and ultimately less time spent with patients. This is where the conversation around AI is shifting; not toward adding more tools but toward removing friction from the workflows that already exist and helping care teams move faster with less overhead inside the flow of care. That reality came through clearly during a recent Microsoft Marketplace Customer Office Hour on Dragon Copilot and Microsoft Marketplace: how to operationalize AI within real-world clinical workflows and enterprise healthcare environments that are experiencing a capacity problem. Instead of focusing on future-state possibilities, the conversation centered on what it takes to move from promise to practice, and where AI can start delivering value today. That distinction matters because developers, healthcare architects, and AI engineers are no longer asking whether AI can create value. The industry has largely accepted that it will play a meaningful role across healthcare. The real challenge is how to integrate into environments already burdened by operational complexity, fragmented workflows, regulatory pressures, and disconnected technologies. In practice, most healthcare organizations aren’t lacking data or systems, they’re struggling with how those systems work together. Clinicians and administrative teams operate across EHRs, reimbursement platforms, documentation tools, referral systems, messaging apps, and care coordination workflows that often function in isolation. Each additional screen, handoff, or disconnected experience introduces friction, and over time that friction compounds into inefficiencies that impact clinicians, administrators, and ultimately patients. This is why AI cannot simply sit on top of existing systems as another productivity layer; it needs to act as an orchestration layer that reduces complexity directly within the flow of care. That shift fundamentally changes how we think about healthcare AI, moving from isolated features to embedded intelligence that supports the workflows where care teams already spend their time. Dragon Copilot as a clinical workflow platform Dragon Copilot is not positioned as just another ambient listening tool or conversational assistant. It's designed as a clinical workflow platform that integrates into how care is delivered. While voice capabilities like ambient listening and natural language interaction are foundational, the real value comes from combining contextual intelligence, workflow automation, and extensibility. In practice, that means clinicians can access relevant information directly within their workflow, reduce fragmentation across systems, and act using natural language without constantly switching between tools. Extending healthcare AI through Microsoft Marketplace What makes this even more compelling is how Dragon Copilot extends through AI apps and agents connected via Microsoft Marketplace. This shifts the conversation from a single AI solution to a broader ecosystem approach. Instead of relying on monolithic systems to solve every problem, healthcare organizations can layer specialized AI capabilities directly into their workflows. During the session, we walked through examples like coding and charge capture, denial prevention, eligibility verification, medication safety checks, and patient education each addressing a specific operational need without requiring organizations to replace core systems. From a technical perspective, what stands out is not just automation, but the ability to reduce workflow re-entry and repetitive administrative loops. Today, many processes require clinicians and administrators to document, submit, reprocess, and reconcile information across disconnected systems. By embedding AI into those workflows, whether for coding validation, reimbursement support, or clinical guidance, organizations can streamline those cycles, improve continuity between systems, and reduce the compounding operational burden that slows teams down. What does this mean for healthcare developers For developers building healthcare solutions, this shift opens meaningful opportunities across workflow orchestration, AI-assisted compliance, operational intelligence, policy validation, and real-time financial support. More importantly, it reflects a broader architectural change in how healthcare technology is evolving. Rather than attempting to replace existing systems, the industry is moving toward connected AI services that extend and augment what’s already in place. This approach matters because healthcare organizations rarely overhaul core infrastructure all at once. Instead, they evolve incrementally by layering new capabilities into existing workflows. Dragon Copilot, combined with Microsoft Marketplace, is designed to support that model. AI agents can surface insights, automate repetitive tasks, and support decision-making while staying embedded within established clinical environments, helping developers build solutions that are practical, scalable, and aligned with how healthcare systems actually operate today. The strategic value of ecosystem extensibility As the importance of ecosystem extensibility continues to grow, Microsoft is intentionally building beyond a standalone healthcare AI solution. Instead, the focus is on creating an ecosystem that enables connected intelligence across clinical and operational workflows. For developers, this shift has real implications. It directly impacts how quickly solutions can be built, how easily they can be deployed, and how far innovation can scale. Without extensibility, progress is constrained by the roadmap of a single platform. With it, developers and healthcare technology providers can target highly specific workflow gaps with purpose-built solutions. That opens the door to a new class of innovations from AI agents and workflow accelerators to embedded clinical decision support and healthcare-specific automation designed to fit seamlessly into existing environments and address the nuanced needs of modern care delivery. Reducing adoption friction in enterprise healthcare The Marketplace component of this strategy directly addresses some of the most persistent barriers to adoption in enterprise healthcare. Organizations can simplify procurement, reduce vendor onboarding friction, streamline licensing, and consolidate billing through Microsoft’s existing purchasing infrastructure. From a developer and software company perspective, this is significant because historically the challenge in healthcare hasn’t been building new capabilities but getting them adopted and scaled in complex environments. By reducing the effort required to evaluate, purchase, deploy, and operationalize AI solutions, Marketplace changes the pace at which organizations can move from experimentation to real-world implementation. That efficiency becomes critical as healthcare shifts from isolated pilots to production-scale deployments, where speed, integration, and operational alignment ultimately determine whether AI delivers meaningful impact. From AI experimentation to production-ready workflows Healthcare AI is no longer confined to pilots or conceptual experimentation. Organizations are now evaluating production-ready solutions that can integrate directly into enterprise workflows. That shift brings a different set of expectations for developers and architects. Instead of asking whether AI can generate useful outputs, the focus has moved to operational questions: Can these systems integrate seamlessly into clinician workflows? Will they reduce complexity without introducing disruption? Can they scale reliably, perform consistently, and meet regulatory requirements? These are not just AI challenges, they’re deeply rooted in systems integration, workflow design, operational engineering, and enterprise architecture. Success depends not only on model performance, but on how well AI fits into the realities of healthcare delivery, supports care teams in context, and operates within the constraints of highly regulated, mission-critical environments. Designing for operational value, not just model innovation This is exactly why the conversation matters for the healthcare developer community right now. Future success in healthcare AI will depend less on model novelty and more on how well those models integrate into real workflows. Most healthcare organizations are already navigating fragmented environments filled with disconnected systems, and the solutions that deliver lasting value will be the ones that reduce cognitive load, minimize context switching, surface information at the right moment, and integrate naturally into day-to-day clinical work. In that sense, the challenge becomes less about AI in isolation and more about systems design. Meaningful progress won’t come from standalone copilots operating outside enterprise infrastructure. It will come from connected ecosystems where AI services, workflow accelerators, and operational tools work together seamlessly. That’s how intelligent healthcare workflows take shape: not as a single application, but as a coordinated system designed around how care is actually delivered. Why this direction matters for the developer ecosystem Dragon Copilot is emerging not just as a healthcare AI experience, but as a platform that brings together workflow intelligence and ecosystem extensibility. By connecting directly into operational healthcare workflows and enabling integration through Microsoft Marketplace, it creates new opportunities for healthcare developers, enterprise architects, and workflow automation providers to build solutions that are both targeted and scalable. While the ecosystem is still evolving, the strategic direction is becoming increasingly clear: AI agents and connected applications are moving closer to the workflow layer itself. In healthcare, that proximity matters. The solutions that integrate most naturally into day-to-day operations, rather than existing alongside them, are the ones most likely to drive meaningful adoption and long-term impact. Watch the full session For organizations building healthcare software, enterprise AI systems, workflow automation platforms, or operational healthcare technologies, the Microsoft Marketplace Customer Office Hour session provides valuable insight into how Microsoft is approaching healthcare AI at ecosystem scale. 👉 Learn more and watch the full session here: Healthcare innovation with Dragon Copilot and Microsoft Marketplace Additional Resources You can learn more through Microsoft Marketplace, the Marketplace Customer Office Hours series, the Microsoft Marketplace Community, and the Dragon Copilot apps and agents resources.Design observability for AI apps and agents selling through Microsoft Marketplace
In the last post, API resilience and reliability patterns for AI apps and agents, we focused on what happens when AI systems encounter failure—and how resilient execution paths keep that failure contained. Timeouts fire with intent. Retries stay bounded. Circuit breakers provide overload protection. When resilience is designed well, your system continues to function even as conditions change, forming the foundation of AI reliability engineering. You can always get curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. Observability for AI systems AI apps and agents are shifting traditional observability, which was designed for systems based on simple assumptions, where requests followed linear paths and workloads behaved predictably. Execution in AI systems consumes tokens at a highly variable rate rather than fixed compute units. Requests unfold across multiple reasoning steps. Agents perform work that spans APIs, models, retrieval layers, and applications. A single interaction may pause, branch, retry, or exit early depending on inferred intent, context, and constraints. Instead of asking whether services are running, observability for AI systems asks: what is the system doing right now—and why? Is an agent spending its time reasoning, waiting on dependencies, retrying tool calls, or exiting early due to enforced limits? Is cost increasing because value is increasing, or because execution paths are expanding without progress? AI observability requirements shift the focus in the following subtle, but critical ways: From resource availability to workflow state From performance metrics to signals From incidents to patterns Core observability dimensions for AI apps and agents Once observability shifts toward understanding behavior, clarity comes from tracking state across the agents in the workflow. For AI apps and agents, observable indicators, such as those detailed below, show how work unfolds and changes during real usage—especially in trials and early adoption: Execution flow shows how a request moves through agents, tools, and workflows. This highlights where execution progresses smoothly, where it slows, and where it concludes early. This makes agent outcomes explainable and keeps behavior consistent across tenants. Cost and token behavior reveals how execution translates into consumption. Token usage per request, per agent step, and per retry shows where value is being delivered and where execution paths expand without proportional benefit. This insight connects runtime behavior directly to Marketplace billing expectations and evaluations. Latency and wait states distinguish active processing from time spent waiting on dependencies. Seeing where time is consumed helps explain slow experiences and guides decisions about optimization, caching, or resilience improvements. Failure classification provides structure when systems degrade and supports effective AI incident management. Separating tool failures from planning failures, and transient issues from terminal exits, keeps investigations focused and prevents protective behavior from being misread as instability. Tenant‑level patterns surface how behavior repeats at scale. Uneven load, and recurring degradation often appear first during trials and shape the customer's perception. Together, these dimensions turn telemetry into understanding—supporting clearer conversations, faster triage, and predictable execution as usage grows. Why observability matters By this point in the journey, your AI app or agent has implemented bounded execution paths, cost controls, and quality of service safeguards. As a result, failure degrades gracefully instead of spreading. These resilience techniques determine how your solution behaves under pressure. The data gathered from observability platforms like Application Insights and Azure Monitor explains why it behaves that way. For AI and agentic systems, infrastructure health alone rarely answers the questions that matter. Services can be up, CPUs can be idle, and queues can look healthy while agents loop inefficiently, retries quietly expand cost, or workflows exit early without delivering value. From the customer’s perspective, the experience feels inconsistent even though the platform appears stable. AI app observability closes this gap by revealing system behavior rather than system status. It shows how requests move, where work concentrates, and how constraints shape outcomes. At Marketplace scale, these patterns repeat across tenants and trials. What appears once during an evaluation often appears again as adoption grows. Observability connects runtime behavior back to the design choices introduced in earlier posts: Usage‑based billing introduced variability in consumption Performance optimization introduced tradeoffs among latency, quality, and cost Resilience patterns introduced controlled failure and bounded execution Observability allows you to explain outcomes during trials, validate assumptions as usage grows, and support post-launch AI operations confidence across customers and environments. Without this visibility, teams react to symptoms. With it, they recognize patterns. From execution paths to behavioral signals Observability begins at the same place resilience begins—API boundaries. These boundaries define where responsibility shifts and where behavior becomes visible. Observability focuses on signals that explain decisions made by the system as it executes instead of relying on raw logs that describe isolated events. Every resilience mechanism emits behavioral signals. Viewed together, these signals provide far more value than logs alone. Logs answer whether something happened. Behavioral signals explain why it happened and how the system responded. Circuit breakers change state as load builds and recedes. Retry loops show whether failures resolve quickly or exhaust their limits. Timeout enforcement reveals where dependencies slow execution. Fallback paths and early terminations show how the system protects itself while preserving outcomes for customers. This perspective matters most for agents. Agent execution unfolds as a series of choices—plan, call a tool, retry, exit early—rather than a single request‑response cycle, which requires monitoring AI agent behavior to remain understandable and consistent at scale. Observability that tracks these decisions makes agent behavior understandable, consistent, and defensible as usage grows across customer tenants. Observability at the agent layer As AI systems become more agent‑driven, observability needs to move closer to where decisions are made. Agents introduce variability by design. They plan, adapt, and choose workflow paths dynamically. Without first‑class visibility into that behavior, execution can appear unpredictable even when the underlying system is healthy. Observability at the agent layer acts as the feedback loop that keeps execution safely bounded. It shows how agents use the freedom you give them—and where that freedom begins to stretch into inefficiency. Observability follows how the agent did its job instead of treating the agent’s interaction as a single outcome. Several indicators help make agent behavior understandable. Step count per request reveals how much reasoning effort a prompt requires. Planning iterations show whether an agent converges quickly or cycles through alternatives. Tool invocation frequency highlights when agents rely heavily on external systems. Early exits compared to full completion explain whether limits and fallbacks activate as designed. Taken together, these indicators help distinguish healthy exploration from inefficient reasoning and degraded execution. An agent exploring briefly before converging adds value. An agent looping through tools without progress signals pressure, uncertainty, or dependency issues. This distinction reinforces a core principle of agentic systems: models reason probabilistically, adapting to context as it changes. Your system observes deterministically—measuring execution, enforcing boundaries, and clarifying outcomes. When those roles stay separate and well‑instrumented, agent behavior becomes transparent, predictable, and ready for Marketplace scale. Observability across environments The type of Marketplace offer you choose shapes what observability customers expect and how responsibility is shared. For SaaS offers, publishers typically own end‑to‑end execution. Observability centers on agent behavior, workflow completion, token usage, latency, and dependency impact across tenants. Publishers rely on consistent signals—often surfaced through tools like Azure Monitor, Application Insights, and Microsoft AI Foundry—to explain how requests behave as scale and load increase. For container‑based offers and Azure Managed Applications, observability expectations are more distributed. Publishers expose clear execution outcomes, limits, and failure signals at application boundaries. Customers, in turn, observe infrastructure health, scaling behavior, and downstream systems within their own environments. This separation ensures each party has visibility into what they control without creating ambiguity. Learn more about Choosing your marketplace offer type for AI Apps and agents. Execution behavior differs across environments for predictable reasons. Scale increases, tenant mix broadens, and external dependencies behave differently under real load. What must stay consistent is how behavior is interpreted. Signal definitions, thresholds, and failure classification should mean the same thing in Dev, Stage, and Prod. Learn more about designing a reliable environment strategy for Microsoft Marketplace AI apps and agents. Staging environments are where this consistency is validated. Observing retries, timeouts, and graceful degradation before production prepares you for Marketplace evaluations, which often resemble production conditions. Observability gaps tend to appear first during customer evaluation—when clarity matters most. Publisher and customer visibility boundaries Purpose: Parallel Post #13 responsibility clarity, now for observability As observability matures across environments, clarity around responsibility becomes essential. For Marketplace solutions, trust grows when publishers and customers each see what they own—and understand where that visibility ends. Publishers are responsible for instrumenting execution paths end to end. That means making workflows traceable, limits visible, and failure modes explainable. Observability should surface behavior—how requests progressed, where execution concluded, and why—rather than exposing raw internal errors that require insider knowledge to interpret. Customers focus their observability on what they control. This includes monitoring downstream systems, infrastructure behavior, and environment‑level alerts within their own estate. When visibility aligns with ownership, teams can act quickly and decisively. Exposing too much internal detail can overwhelm customers and blur accountability. Observing too little behavior creates friction, especially when issues cross boundaries and lack context. Clear visibility enables faster triage, sharper ownership boundaries, and fewer escalations rooted in ambiguity. Observability as an enabler for scale, billing, and trust From a customer’s perspective, observability answers two fundamental questions: Can I understand what happened? and Can I trust this at scale? When the answer to both is clear, observability becomes part of the value your Marketplace offering delivers. When system behavior is visible and explainable, customers gain confidence that adoption and growth will remain predictable. Observability directly supports usage‑based billing by tying execution behavior to measured consumption. Clear visibility into token usage, retries, and execution paths helps validate how usage is calculated and supports transparent billing conversations. It also enables ongoing performance tuning and caching strategies by showing where latency accumulates, where work repeats, and where optimization delivers measurable impact. Observability reinforces confidence in resilience mechanisms, confirming that limits, fallbacks, and degradation paths activate as designed under real‑world conditions. Beyond validation, observability creates a continuous feedback loop. Execution data informs pricing adjustments, guides changes to limits, and helps refine default configurations as customer behavior evolves. What’s next in the journey With execution behavior observable and explainable, the focus shifts to how AI systems are operated safely as change accelerates. The upcoming posts will discuss deployment strategies, CI/CD pipelines for agents, and progressive rollouts build on this foundation—ensuring AI apps evolve confidently as usage and expectations grow. Key Resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessDiscover new Microsoft Marketplace innovations announced at Microsoft Build
At Microsoft Build, Microsoft shared new opportunities for software development companies and partners to build, scale, and monetize AI apps and agents through Microsoft Marketplace. Explore how Microsoft Marketplace is helping software companies accelerate go-to-market strategies, expand customer reach, simplify procurement, and unlock new revenue opportunities across the Microsoft ecosystem. Learn how organizations can take advantage of Azure and Marketplace capabilities to support AI innovation and deliver enterprise-ready solutions faster. Whether you’re building intelligent applications, growing your commercial marketplace presence, or exploring new ways to monetize AI-powered solutions, this is a valuable resource for understanding the latest Microsoft Marketplace announcements and opportunities coming out of Build. 👉 Read more: Build, scale, and monetize apps and agents with Microsoft MarketplaceAccelerate your AI or agent build to sell on Marketplace with Quick-Start Development Toolkit
Want to skip right to coding in minutes? Start with the interactive wizard in App Advisor Building AI products quickly is becoming table stakes. Building them in a way that supports scalability, repeatability, and a path to commercialization is where software companies create advantage. The challenge now is reducing the time between identifying an opportunity and getting developers working inside a proven structure that supports real deployment outcomes. That’s where the AI, agentic, and Copilot branch of the Quick-Start Development Toolkit helps. Embedded directly within App Advisor, Quick-Start Development Toolkit helps software companies move from concept to implementation faster using guided development patterns, trusted architectures, deployable reference code, and practical resources designed to reduce friction across the development process. Build AI & agentic products faster without starting from scratch Development teams often know the customer scenario they want to solve. What slows momentum is deciding where to begin, selecting architecture patterns, and aligning implementation decisions across teams. The Quick-Start Development Toolkit helps remove that uncertainty. By answering a few focused questions about what you want to build, who it serves, and the products you’re building with, you’re matched with a development pattern designed to accelerate execution. Each development pattern includes: Self-serve, click-to-deploy reference code aligned to your scenario, Sample solution architecture to help visualize products and reduce guesswork, and Practical how-to resources and implementation guidance to overcome friction points, Everything is structured to support faster decision making and help teams move confidently into development. Accelerate development with purpose-built AI accelerators The AI and agent branch of Quick-Start Development Toolkit includes development accelerators designed around high-value scenarios, so your team can spend less time assembling foundations and more time building differentiated experiences. Each of these accelerators is built and fully maintained by Microsoft experts, so you can be confident your code template isn’t stale. Our most popular accelerators include: Multi-Agent Custom Automation Engine Accelerator: Delegate complex, repetitive tasks to AI agents that act on your behalf—executing work efficiently, reducing manual effort, and ensuring results align with your organization's standards. Conversation Knowledge Mining Accelerator: Improve contact center performance with AI-powered conversation intelligence—analyzing audio and text data on a large scale to show insights, improve service, and drive smarter decisions. Accelerate agentic applications for Unified Data Foundations (with Microsoft Fabric): Accelerate decision making at scale with secure, agentic AI built on a unified data foundation with two use cases for sales performance and customer insights. Each pattern includes common use cases, related resources, and pathways to adjacent scenarios so teams can continue progressing without losing momentum. The goal is to help your team move from experimentation to a product that can be packaged, deployed, and prepared for customers. You can see more of our accelerators here Coming this week: The Microsoft IQ solution accelerator leverages a shared intelligence layer to unify data, knowledge, and workflows, enabling AI-powered insights and coordinated actions for measurable business outcomes. Build with Microsoft Marketplace outcomes in mind Development choices shape commercial outcomes. Starting with trusted architecture and structured implementation guidance can help reduce redesign cycles later when preparing to package, publish, and scale. Quick-Start Development Toolkit helps software companies: Shorten time from idea to deployable AI product, Improve alignment across implementation decisions, Reduce development overhead through reusable foundations, and Create repeatable pathways toward publishing and selling. When development starts with clarity, commercialization becomes easier. Keep moving forward with App Advisor Quick-Start Development Toolkit is embedded within App Advisor because building is only one stage of the journey. App Advisor helps connect decisions across design, development, publishing, and growth so teams can continue moving forward with less context switching and more confidence. As your solution evolves, App Advisor provides curated, step-by-step guidance to help you prepare for Marketplace readiness and make the next decision faster. Ready to start? Explore Quick-Start Development Toolkit Start where you need help with App Advisor
