惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

有赞技术团队
有赞技术团队
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
aimingoo的专栏
aimingoo的专栏
IT之家
IT之家
G
Google Developers Blog
爱范儿
爱范儿
博客园 - 司徒正美
Recent Announcements
Recent Announcements
The Register - Security
The Register - Security
J
Java Code Geeks
The Cloudflare Blog
M
MIT News - Artificial intelligence
Apple Machine Learning Research
Apple Machine Learning Research
Microsoft Security Blog
Microsoft Security Blog
博客园 - Franky
雷峰网
雷峰网
酷 壳 – CoolShell
酷 壳 – CoolShell
Blog — PlanetScale
Blog — PlanetScale
Vercel News
Vercel News
宝玉的分享
宝玉的分享
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
B
Blog
小众软件
小众软件
Microsoft Azure Blog
Microsoft Azure Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
WordPress大学
WordPress大学
T
Troy Hunt's Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
H
Hacker News: Front Page
H
Help Net Security
S
Security @ Cisco Blogs
V
V2EX
Security Archives - TechRepublic
Security Archives - TechRepublic
Stack Overflow Blog
Stack Overflow Blog
O
OpenAI News
L
LINUX DO - 最新话题
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
S
Secure Thoughts
Help Net Security
Help Net Security
F
Full Disclosure
博客园 - 叶小钗
The Hacker News
The Hacker News
Spread Privacy
Spread Privacy
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Jina AI
Jina AI
K
Kaspersky official blog
www.infosecurity-magazine.com
www.infosecurity-magazine.com
V
Vulnerabilities – Threatpost
P
Privacy International News Feed
Scott Helme
Scott Helme

InfoWorld

AWS boosts CloudWatch Logs query limits by 10x to ease debugging for developers, SREs 21 LLMs tuned for special domains AWS adds Advanced Prompt Optimization tool to Bedrock Capacity markets could reshape cloud computing Four cutting-edge tools for spec-driven development Anthropic puts Claude agents on a meter across its subscriptions Notion courts developers with a platform for AI agents and workflow automation Using continuous purple teaming to protect fast-paced enterprise environments A better way to work with SQL Server Evidence-driven workflows: Rethinking enterprise process design AWS debuts Graviton-powered Redshift RG instances to cut analytics costs SAP’s AI promises last year? Most are still rolling out First look: Lemonade serves up local AI with limitations GitLab CEO sees developer tool bill increasing 100-fold Red Hat adds support for agentic AI development What’s new and exciting in JDK 26 Kill the loading spinner with local-first data and reactive SQL A networking revolution at AWS Tokenmaxxing is super dumb Hands-on with React, Supabase, and PowerSync How to add AI to an existing product (without annoying users) Your AI doesn’t need another database What happens when engineering teams reorganize around AI agents Python isn’t always easy When cloud giants meddle in markets 12 model-level deep cuts to slash AI training costs The best new features in Python 3.15 Teradata launches platform for enterprise AI agents moving beyond pilots Three skills that matter when AI handles the coding MongoDB targets AI’s retrieval problem Building AI apps and agents with Microsoft Foundry Designing front-end systems for cloud failure No, AI won’t destroy software development jobs Diskless databases: What happens when storage isn’t the bottleneck Vibe coding or spec-driven development? The agentic AI distraction Vibe coding or spec-driven development? How to choose Cloud providers are blinded by agentic AI SAP to acquire data lakehouse vendor Dremio Small language models: Rethinking enterprise AI architecture Making AI work through eval hygiene Improving AI agents through better evaluations AI in the cloud is easy but expensive Running AI in the cloud is easy – and expensive Making AI work for databases Harness teams of agentic coders with Squad Harness teams of coding agents with Squad Oracle NetSuite announces AI coding skills for SuiteCloud developers Why it’s so hard to create stand-alone Python apps A new challenge for software product managers The hidden cost of front-end complexity GitHub shifts Copilot to usage-based billing, signaling a new cost model for enterprise AI tools OpenAI’s Symphony spec pushes coding agents from prompts to orchestration The front-end architecture trilemma: Reactivity vs. hypermedia vs. local-first apps Enterprise AI is missing the business core The best JavaScript certifications for getting hired Google begins putting the guardrails on agentic AI Why world models are AI’s next frontier Where to begin a cloud career Google pitches Agentic Data Cloud to help enterprises turn data into context for AI agents How open source ideals must expand for AI Is your Node.js project really secure? How I doubled my GPU efficiency without buying a single new card SpaceX secures option to acquire AI coding startup Cursor for $60B Google’s Gemma 4 shines on local systems – both big and small AI is upending the SaaS game How AI is upending SaaS tools Snowflake offers help to users and builders of AI agents From the engine room to the bridge: What the modern leadership shift means for architects like me Addressing the challenges of unstructured data governance for AI The cookbook for safe, powerful agents Enterprises are rethinking Kubernetes GitHub pauses new Copilot sign-ups as agentic AI strains infrastructure Best practices for building agentic systems Making agents dull Oracle delivers semantic search without LLMs When cloud giants neglect resilience Exciting Python features are on the way Ease into Azure Kubernetes Application Network The agent tier: Rethinking runtime architecture for context-driven enterprise workflows The two-pass compiler is back – this time, it’s fixing AI code generation MuleSoft Agent Fabric adds new ways to keep AI agents in line Salesforce launches Headless 360 to support agent‑first enterprise workflows Tap into the AI APIs of Google Chrome and Microsoft Edge Where will developer wisdom come from? GitHub adds Stacked PRs to speed complex code reviews The hyperscalers are pricing themselves out of AI workloads HTMX 4.0: Hypermedia finds a new gear Google Cloud introduces QueryData to help AI agents create reliable database queries Hands-on with the Google Agent Development Kit Are AI certifications worth the investment? AWS targets AI agent sprawl with new Bedrock Agent Registry Cloud degrees are moving online Swift for Visual Studio Code comes to Open VSX Registry AI agents aren't failing. The coordination layer is failing Anthropic rolls out Claude Managed Agents Microsoft’s reauthentication snafu cuts off developers globally Meta’s Muse Spark: a smaller, faster AI model for broad app deployment Bringing databases and Kubernetes together AWS turns its S3 storage service into a file system for AI agents
Build an agent? Sell an agent
by Simon Bisson Contributing Writer · 2026-06-11 · via InfoWorld

Microsoft Marketplace is becoming a useful resource for AI application developers, offering tools to build agents, models to power them, and a way to monetize them.

Modern AI systems have evolved beyond the simple chatbots that quickly became popular. Now they use semantic tools to manage workflows and link machines to machines, providing a flexible and effective framework for the next generation of business automation. What you used to build in Microsoft’s Power Platform or construct inside Biztalk is now an agent, built around large language models (LLMs) that can parse both your data and the APIs that you want to use your data with, orchestrating workflows with a level of autonomy that traditional tooling can’t match.

That shift has offered new opportunities, much like those that came with business platforms like Microsoft Dynamics and Salesforce. Here, tools built to solve one set of business problems could be turned into applications that could be sold to other companies. What worked for you to solve one of your problems could now be an added revenue stream, sold through platform marketplaces that helped customers manage installations and customizations.

Agents are business applications now

Modern agents are much like those business applications. Often developed to solve a specific need, but quicky adopted by organizations and refactored to apply enterprise standards (using tools like the Agent Governance Toolkit and frameworks like Microsoft’s Agent Framework), they’re rapidly maturing and are ready to be shared more widely. The process of sharing needs to be curated and controlled, and, if possible, tied to a revenue stream.

There’s certainly some urgency here. Until recently, subsidized tokens have kept costs artificially low. Now companies like GitHub and Anthropic are moving to a more sustainable (for them) pricing model, increasing the cost of inferencing and squeezing companies’ AI budgets. As a result, switching AI projects away from a cost to a revenue source is high on CIOs’ agendas. If those tuned and trained agents can be sold on a marketplace, then that token budget can be justified.

Microsoft has always been a company built on partner relationships, starting with individual developers and working all the way up to the largest software companies and consultancies. That reach is key to helping partners extract as much value as possible from their agents, as it allows Microsoft to integrate its partner sales tools into its own products and services, as well as into other platforms.

Extending the Microsoft Marketplace for AI developers

We’re already familiar with many of Microsoft’s marketplaces, built into individual tools like Teams, into platforms like Microsoft 365 or Visual Studio, or into the Windows Store. Now the company is doing the same for AI developers, extending Microsoft Marketplace to software agents. Announced at Build 2026, the updated Microsoft Marketplace provides ways to publish code—apps and agents—developed across all of Microsoft’s development platforms, including Copilot Studio, opening the marketplace up to traditional and non-traditional developers alike.

Perhaps the most important aspect of this new Marketplace is its own intelligence, using context to expose your code to the right audience. If you’ve developed an agent for use with Microsoft 365, it will be exposed inside the Microsoft 365 Copilot Agent Store; for Visual Studio, in the Visual Studio Marketplace; or for Teams, in the Microsoft Marketplace. All of these are different views on the same back end, using AI to ensure that the relevant agents are displayed.

Replacing search with Intelligent Discovery

This is extended by another new service, Intelligent Discovery, which adds natural language support to search, using AI to infer user intent and highlight the most relevant tools. Building on the familiar metaphor of the search bar, the initial smart search model offers a freeform way to explore the Marketplace. While there are suggested prompts, they’re not necessary. The search tooling allows you to generate comparisons between tools using your own criteria, with the Marketplace AI generating views based on your requirements.

Microsoft’s aim here is to shift discovery from keywords to use cases, so that buyers can quickly get the tools they need without having to evaluate different solutions, before completing a purchase. By handing that aspect of the buying process over to Marketplace’s AIs, customers can go straight to trials or even to buying agents and applications.

For a tool like this to be successful it needs to be trustworthy. By building it on top of the same development frameworks as your agents, Microsoft can take advantage of the AI guardrails built into Microsoft Foundry as well as low-level tooling like the Agent Governance Framework. Restricting the intelligent search to the Marketplace catalogue reduces the risk of hallucination, as output is grounded in Marketplace data and metadata.

A developer-friendly marketplace

Microsoft is providing tooling to help developers get their listings right. According to Cyril Belikoff, Microsoft’s vice president of Commercial Cloud and AI, “We actually have a separate AI tool that we give to software companies to optimize their listings, called a listing optimizer, funny enough, and that listing optimizer reviews their listing and then provides them with particular guidance on how to best improve it, so that it can be best discoverable in today’s search world.”

You can expect the listing optimizer to be tuned to work with the new Marketplace tooling, but for now it still focuses on traditional search. As Marketplace is a B2B platform, there’s a lower risk of spam applications, but even so, Microsoft remains aware of the possibility of a new system being gamed, and will be rolling out Intelligent Discovery carefully, monitoring its performance as more customers get access over time.

Having a new discovery method is one thing; getting quality AI applications in the Marketplace is another. Microsoft is validating all code submitted, though the criteria will differ between target platforms. An agent built for Teams will be treated differently than one built on Microsoft 365’s WorkIQ. It’s an approach that allows Microsoft to support new standards as they become available.

Alongside its agent development tooling, Microsoft is rolling out a new set of guidelines and processes to help developers get ready to sell their agents. Hosted on GitHub, these offer code templates as well as a link to the App Advisor guidance tools.

Still gaps to fill

This first release of Intelligent Discovery is promising, but some key features are missing. With agent token costs an increasing problem for businesses, it would be nice to see tools that help predict costs, integrating with finops tooling. We’re living in an age of shadow AI, and putting the AI we used to buy with credit cards in Microsoft Marketplace is one way to shine a light on those shadows — bringing the necessary control and governance to AI purchases, and maybe even providing support for site licensing.

Microsoft Marketplace is becoming a useful resource for AI application developers. It encompasses the entire development life cycle: offering tools that can help you build agents, the models that you need to power your agents, and finally a way to monetize your work. There’s a longer-term opportunity here, for both the Marketplace and Intelligent Discovery to offer Model Context Protocol (MCP) interfaces, ensuring that tooling and tool discovery become part of the developer workflow, and making developers aware of new tools that might help solve a problem or simplify a task.