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 SuccessPartner Blog | Building AI-ready applications on open, enterprise-grade Azure platforms
Microsoft Build 2026 reinforced a practical reality for organizations moving from AI experimentation to production: AI is only as strong as the foundation it runs on. Customers need modern databases, governed data, secure infrastructure, and developer experiences that can carry performing AI-enabled applications at scale into production with confidence. The public preview announcements of Azure HorizonDB and Azure Linux, along with the general availability of Azure Container Linux, show how Microsoft is investing in open platforms, developer ecosystems, and enterprise-grade cloud infrastructure for AI-ready applications. These announcements also point to a broader shift: open source has become a strategic foundation for enterprise modernization, innovation, and strategic growth. These announcements give partners a straightforward way to connect customer AI goals to the open, secure, enterprise-ready platforms needed for production. They also create timely opportunities to engage customers on data modernization, AI-ready application development, secure infrastructure, and cloud-native operations. What was announced at Build 2026 Azure HorizonDB: A new standard for AI-native, enterprise PostgreSQL Azure HorizonDB enters public preview as a new PostgreSQL cloud database service engineered for performance, scale, and modern AI-powered application needs. For business leaders thinking about data strategy, this is significant. Organizations are under pressure to modernize legacy databases and support intelligent applications without sacrificing resilience, governance, or developer productivity. Azure HorizonDB is designed to address those priorities with a platform that can scale storage automatically for large enterprise workloads, scale compute across primary and replica nodes, and bring AI-native capabilities directly into the database layer. What stands out is that Azure HorizonDB gives enterprises a way to simplify architecture while also accelerating innovation. Features such as advanced filtered vector search, in-database AI model management, Microsoft Entra ID integration, and GitHub Copilot integration through the PostgreSQL extension for Visual Studio Code position it as more than a database modernization story. Developers can use Microsoft 365 Copilot with live database context to generate schema-aware SQL, explore database structures, analyze and rewrite queries, and build against HorizonDB-specific capabilities without leaving Visual Studio Code. It is designed to bring together open-source PostgreSQL, enterprise security, AI readiness, and a more integrated developer experience in a single managed service. For business leaders, that can create a faster path from data estate modernization to measurable business outcomes. In Microsoft internal testing environments, Azure HorizonDB performed three times faster than self-managed PostgreSQL. For partners, the announcement creates an opportunity to engage customers in PostgreSQL modernization, intelligent application architecture, migration planning, performance optimization, and AI-enabled development. Azure Linux: Open-source infrastructure at enterprise scale The Azure Linux public preview announcement is meaningful for leaders focused on cloud efficiency, security, and platform consistency. Linux is already foundational to modern digital infrastructure, and two-thirds of customer cores in Azure run Linux. Now Azure Linux provides a first-party Linux distribution, purpose-built for Azure, and is available for Azure virtual machines (VMs), VM scale sets, and container images. We also announced Azure Container Linux (ACL), a secure, immutable container host designed to help platform teams run Kubernetes workloads at scale on Azure Kubernetes Service (AKS). By bringing Azure Linux forward as a more visible first-party platform choice, Microsoft is giving organizations a cloud-native operating system designed for modern workloads, including virtual machines (VMs), containers, and AI infrastructure. This matters because infrastructure choices increasingly shape agility, security posture, and operating cost. Azure Linux reflects the Microsoft focus on secure-by-default design, consistent servicing, and tighter alignment between the operating system and the Azure platform. For enterprises, that can translate into simpler operations and a more predictable foundation for cloud-native applications. These announcements reinforce what the Microsoft partner ecosystem and customer usage have shown for years: open-source infrastructure is foundational to Microsoft cloud strategy, as more than 65% of customer cores in Azure run Linux. Continue reading blog herePatner Case Study | Pax8
From their early days of selling Microsoft licenses to becoming a leading cloud commerce marketplace, Pax8 has always centered their business on the Microsoft partner experience. And their future-focused, partner first approach is redefining what it means to be a modern Cloud Solution Provider (CSP). For Pax8, the next evolution of that role is the Managed Intelligence Provider (MIP)—a partner that moves beyond managing systems to orchestrating intelligence and delivering measurable business outcomes with AI. While Pax8 delivers critical value through the streamlined billing and provisioning of Microsoft licenses, they also know that real business transformation doesn’t happen just because you’ve purchased software—it happens because of everything that comes after the sale. That’s why they invested heavily in enablement, education, and professional services. And that’s why today, they’re successfully cultivating an ecosystem that drives meaningful, scalable growth for partners and customers alike. Grounded in their experience as a member of the Microsoft AI Cloud Partner Program, Pax8’s approach has changed the trajectories of organizations like Sourcepass, a US-based managed services provider (MSP). Together, Sourcepass and Pax8 are making enterprise-grade AI accessible to small and medium-sized businesses (SMBs)—but they’re going beyond traditional deployments. They’re delivering functional, agentic solutions designed to help customers realize value and support recurring service opportunities. Navigating AI adoption in the SMB reality SMBs are under constant pressure to do more with less, and the business outcomes they need the most—efficiency, strong security, scalable growth—are the exact outcomes AI can deliver. But with limited budgets, it’s hard for SMBs to justify the spend without clear proof of value—no matter how interested they might be. “SMBs hear about AI and want to be part of the wave,” said Elliott Eliason, Productivity Solution Consultant at Pax8, “but they don’t want to spend thousands without getting results back.” And once they’ve made an investment in AI, they likely lack the internal resources to operationalize it. That includes preparing data governance, validating security controls, and teaching teams new workflows. For a partner like Sourcepass, who primarily serves SMBs, this means their customers need trusted, strategic advisors who can guide them in turning AI’s potential into tangible outcomes. Continue reading here Explore all case studies or submit your own Subscribe to case studies tag to follow all new case study posts. Don't forget to follow this blog to receive email notifications of new stories!
From insight to action: how Adobe and Microsoft are helping marketers move faster with AI
Today’s marketing leaders are under pressure to do more than ever—deliver meaningful personalization, accelerate execution, and prove measurable business impact. At the same time, teams are navigating increasing complexity: fragmented data, disconnected tools, and insights that arrive too late to act on. AI can change this—but only when it’s embedded directly into how people already work. That’s why Microsoft and Adobe are deepening our partnership: bringing customer experience intelligence, AI-powered workflows, and enterprise-grade AI directly into Microsoft 365 Copilot—so teams can move from insight to alignment to execution in one continuous workflow. The result is faster decisions, more coordinated execution, and clearer business outcomes—without breaking flow or context. Bringing customer experience intelligence into the flow of work Marketing teams don’t struggle because they lack data. They struggle because insights live in one place, collaboration in another, and execution somewhere else entirely. That disconnect slows teams down and creates unnecessary friction between analysis and action. Together, Adobe and Microsoft are changing that dynamic by connecting Adobe’s customer experience capabilities with Microsoft 365 Copilot and Copilot Cowork—so insight, collaboration, and next-best action can happen where work already happens: in Copilot Chat and in everyday apps like Teams, Word, and PowerPoint. Marketers can ask questions, explore insights, align with teammates, and take action without jumping between tools—turning intelligence into impact at the moment it matters. Adobe Marketing Agent for Microsoft 365 Copilot: now generally available A major milestone in this journey is the general availability of the Adobe Marketing Agent for Microsoft 365 Copilot, now available via Microsoft Commercial Marketplace. The Adobe Marketing Agent brings Adobe customer experience intelligence directly into Copilot, enabling marketing teams to: Accelerate time from insight to decision Move seamlessly from analysis to execution Keep humans firmly in control, with AI supporting—not replacing—decision‑making Importantly, the agent is enterprise-ready by design. IT administrators can deploy and manage the experience through the Microsoft 365 admin center, ensuring security, governance, and compliance at scale. Expanding executive experiences with Copilot Cowork Looking ahead, Adobe skills designed for customer experience orchestration will be accessible in Copilot Cowork—in a future release. This upcoming experience will enable customer experience leaders to engage with customer experience insights in a more direct, conversational way, bringing strategic visibility into the same Copilot environments where decisions are made and actions are coordinated. Built on Azure to scale securely and responsibly The technology foundation of this innovation is Azure. Adobe Experience Platform, Adobe Experience Platform Agent Orchestrator, and Adobe AI Agents are built on Azure and leverage Azure AI models, providing the scalability, security, and reliability enterprises require. By running on Azure, these agentic experiences benefit from Microsoft’s global infrastructure, enterprise‑grade security, and responsible AI commitments—supporting customer trust as organizations scale AI across their business. Designed for interoperability across agent ecosystems Modern enterprises don’t operate in a single ecosystem—and their agents shouldn’t either. Adobe agents are built to interoperate with agents created using Microsoft Azure AI Foundry or Copilot Studio, enabling customers to orchestrate richer, cross‑functional workflows across marketing, sales, service, and operations. This architecture is designed to enable organizations to compose agentic solutions that reflect how work actually happens—across systems, teams, and business processes. Moving from experimentation to execution This partnership reflects a broader shift in how organizations adopt AI—moving from experimentation to embedded, enterprise‑ready execution. By bringing the full power of Adobe Experience Platform together with Microsoft’s AI platform, cloud infrastructure, and Copilot experiences, we’re helping teams move faster with clarity, confidence, and control. This is how AI becomes not just powerful—but practical. Learn more Adobe + Microsoft partnership page Adobe Marketing Agent for Microsoft Copilot pageICYMI | Explore curated Microsoft Partner Digital Skilling Journeys
Skilling is foundational for success in an AI-first market. Keeping up with evolving technologies and capabilities better positions you with the skills you need to succeed in an ever-changing market, making it a base-level requirement for sustained growth. As part of our effort to support partners with skilling, we offer Microsoft Partner Digital Skilling Journeys (PDJs), curated presales, sales, and tech skilling experiences designed to enhance your knowledge aligned with partner strategic wins. First-time users may need to register—use your work email credentials to sign up. Curated journeys include: Continue reading this article on our new Partner Skilling discussion board Be sure to click follow in the top right to be notified of all new announcements!GitHub Copilot is moving to usage-based billing
Instead of counting premium requests, every Copilot plan will include a monthly allotment of GitHub AI Credits, with the option for paid plans to purchase additional usage. Usage will be calculated based on token consumption, including input, output, and cached tokens, using the listed API rates for each model. This change aligns Copilot pricing with actual usage and is an important step toward a sustainable, reliable Copilot business and experience for all users. Learn more here and access partner resources here. APAC Office hours link – May 6, 7:00 PM — 8:00 PM PDT EMEA/AMER Office hours link – May 7, 8:00 AM — 9:00 AM PDTBuild AI skills at Microsoft AI Skills Fest
Register now for Microsoft AI Skills Fest, a no-cost digital skilling event taking place June 8–12. Designed so partners can stay ahead in the rapidly evolving AI landscape, attendees can build in-demand AI skills, gain practical insights, and discover ways to deliver innovative solutions, deepen customer relationships, and stay competitive in a fast-changing market. Get ready to lead Frontier Transformation If you’re a solution engineer or architect building AI agents across the Frontier stack—including Microsoft 365 Copilot, Microsoft Foundry, Copilot Studio, GitHub Copilot, Fabric, and Agent 365—you can validate your expertise with the Frontier Transformation Engineer badge. Continue reading this blog on our newSkilling discussion boardDiscover 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 MarketplacePartner Blog | How partners are using AI as a strategic accelerator to drive business goals
Partners from across the Americas have been sharing their innovative AI concepts with us this year. We’re seeing several trends, but one has been particularly prominent: using AI to support business acceleration. From brainstorming to evaluating ideas, partners are using tools like Microsoft Copilot to turn concepts into action faster. In this blog, we’ll explore how Microsoft partners like BNY, Cisco, and GFT Technologies are integrating AI into their planning and strategy efforts. Reclaiming time for innovation at BNY Before AI, analyzing data for insights was a time-consuming and resource-intensive pursuit. Although there were tools available, it was still a highly manual process. AI supports partners in potentially cutting time to insight from days to minutes. Partners still need the right data and prompts, but the potential for faster and better responses is there. However, speed alone doesn’t create a competitive advantage. Faster insights may lead to greater output, but that doesn’t ensure better outcomes. The shift happens when that reclaimed time is deliberately redirected toward higher-value work such as revisiting business priorities, identifying new growth opportunities, and aligning teams around what matters most. In practice, this means using AI not just to answer questions but to inform which questions should be asked next. “AI has changed leadership in a way that we can move forward in our strategic initiatives,” says Deanna Lanier, Head of Data & Analytics Strategy and New Product Innovation at BNY. “Now, we can place more innovation into our processes, and we can certainly start thinking about the organization and how we change it from a digital perspective.” Enabling insights is just the first step in BNY’s journey. The financial institution is reviewing both internal and external operations through an AI lens. Some opportunities to enhance existing practices have already been identified. With faster time to insight, BNY now has time to convert those opportunities into action. Using AI as a brainstorming partner at Cisco There are many ways to use AI, but research seems to be the most well-known. Many newer users see AI as an advanced search engine. Experienced users don’t simply ask it to generate content; however, they use it as a strategic accelerator. Continue reading blog hereAccelerate 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
