Build observability for scalable AI apps and agents selling through Microsoft Marketplace
Discover how to design observability for AI apps and agents selling through Microsoft Marketplace. This Marketplace Community article explains why visibility into execution behavior is essential for operating AI systems confidently at scale—not just keeping them running. As AI apps and agents reason, branch, retry, and exit dynamically at runtime, traditional infrastructure metrics fall short. Behavioral signals such as execution flow, token usage, latency, and failure patterns help explain what systems are doing, why outcomes occur, and how limits and safeguards shape behavior across tenants and environments. Learn how observability turns runtime telemetry into clarity that supports customer trust, usage‑based billing, and scalable operations. Read more: Design observability for AI apps and agents selling through Microsoft MarketplaceDesign resilient API execution for AI apps and agents in Microsoft Marketplace
Discover how to design resilient and reliable APIs for AI apps and agents selling through Microsoft Marketplace. This Marketplace Community article explains why reliability is a critical readiness requirement and how predictable API behavior protects customer trust when systems encounter failure. AI apps and agents depend on APIs for every stage of execution—from model inference to tool calls and orchestration. Designing clear API boundaries, enforcing timeouts and retries, and applying resilience patterns such as circuit breakers and bulkheads helps contain failures, prevent cascading issues, and keep agent behavior bounded as solutions scale. Learn how to design AI systems that fail safely, operate predictably, and remain stable under real customer load across Marketplace environments. Read more: API resilience and reliability patterns for AI apps and agents selling through Microsoft MarketplaceBuild and scale AI apps and agents for Microsoft Marketplace success
Ready to move from AI concept to Marketplace success? This article walks through the end-to-end journey of building, publishing, and commercializing AI apps and agents on Microsoft Marketplace—helping software companies navigate the full lifecycle with confidence. From architecture and security to compliance, operations, and commerce integration, Marketplace readiness requires more than a working model—it demands a holistic, production-ready approach. This post introduces a connected framework to guide every stage of development, ensuring your solution is designed for real-world deployment, scalability, and customer trust. Whether you’re refining a prototype or preparing for launch, discover how to align your AI solution with Marketplace requirements from day one and accelerate your path to revenue. Read the full article: Success with AI apps and agents in Marketplace: the end-to-endDesign predictable AI performance to scale selling through Microsoft Marketplace
Discover how to design predictable AI performance that scales when selling apps and agents through Microsoft Marketplace. This Marketplace Community article explains why performance variability can undermine customer trust and how intentional design boundaries help customers evaluate value with confidence. As AI systems reason, retrieve data, and orchestrate tools at runtime, latency, quality, and cost become tightly linked. Bounded execution paths, cost‑aware orchestration, and layered performance controls help ensure responses remain fast, reliable, and predictable—without sacrificing output quality as usage grows. Learn how to design AI architectures that customers can trial confidently, scale sustainably, and operate without surprises across Marketplace environments. Read more: Design Predictable AI Performance for Apps Selling Through Microsoft MarketplaceDesign predictable usage-based billing for AI apps and agents in Microsoft Marketplace
Discover how to design predictable usage‑based billing for AI apps and agents selling in Microsoft Marketplace. This Marketplace Community article explains why traditional software pricing models break down for AI systems and how usage‑based billing can align costs with real customer value. As AI apps and agents reason, infer, and operate dynamically at runtime, usage—and cost—can vary significantly. Clear plan design, well‑defined metered dimensions, and pricing models aligned to business outcomes help customers understand what’s included, forecast spend, and trust how charges are applied as usage scales. Learn how to design billing that customers can explain to finance teams, procurement can approve, and Marketplace solutions can scale without surprises. Read more: Design predictable usage‑based billing for AI apps and agents selling in Microsoft MarketplaceHow to design production-ready AI architectures for apps and agents on Microsoft Marketplace
As organizations accelerate the development of AI-powered applications and agents, moving from prototype to production requires a strong architectural foundation. This article explores what it takes to build AI solutions that are ready for enterprise deployment through Microsoft Marketplace—where reliability, security, scalability, and operational readiness are essential. Learn why “production-ready” architectures are critical to meeting customer expectations, how early design decisions impact long-term success, and what patterns software development companies should consider when aligning solution architecture with Marketplace offer types. The article also highlights key considerations around ownership, runtime environments, and operational responsibilities that shape how AI solutions are deployed and supported. Whether you are building your first AI app or scaling an existing solution, this guidance provides a practical foundation for designing trusted, enterprise-ready offerings that customers can confidently run in production. Read the full article: Production ready architectures for AI apps and agents on Marketplace | Microsoft Community Hub
