Agent Skills Are Quietly Becoming the Industry’s AI Plumbing—Here’s What .NET Developers Need to Know

TL;DR: Anthropic donated its Model Context Protocol (MCP) to the Linux Foundation on December 9, and both Anthropic and OpenAI co-founded the Agentic AI Foundation alongside Block . OpenAI has quietly adopted structurally identical architecture in both ChatGPT and its Codex CLI tool, mirroring Anthropic’s specification—the same file naming conventions, the same metadata format, the same directory organization . For .NET developers on Azure, this means your agent integrations just got a lot simpler—and a lot more portable.

The Quiet Convergence

For months, the AI industry seemed fragmented. OpenAI had one way to hook models into tools. Anthropic had another. Google had yet another. But something remarkable just happened: the industry has found a common answer to a vexing question: how do you make AI assistants consistently good at specialized work without expensive model fine-tuning .

The mechanism? Skills—modular, versioned, governed building blocks that let agents handle real work reliably. Anthropic introduced a feature that looked like a developer tool two months ago; today, that feature has become a specification that Microsoft builds into VS Code, that OpenAI replicates in ChatGPT, and that enterprise software giants race to support .

What This Means for Azure Developers

If you’re shipping agents on Azure, this is your moment. Azure is now the only cloud providing access to both Claude and GPT frontier models to customers on one platform . But the real win is the standardization underneath.

Skills in Microsoft Foundry

With the Claude API, developers can define skills—modular building blocks that combine instructions, context, and tools. Skills automate workflows like generating reports, cleaning datasets, or assembling PowerPoint summaries and can be reused or chained with others to form larger automations. Within Microsoft Foundry, every Skill is governed, traceable, and version-controlled, ensuring reliability across teams and projects .

In practice, this means your agent can do something like this:

// Pseudo-code: defining a skill in Foundry
var reportGenerationSkill = new AgentSkill
{
    Name = "GenerateMonthlyReport",
    Description = "Pulls data from Azure Cosmos DB, synthesizes insights, and outputs a formatted report",
    Tools = new[] { "CosmosDbQuery", "TextFormatting" },
    GovernancePolicy = "AuditTrail"
};

agent.RegisterSkill(reportGenerationSkill);

The skill is discovered dynamically, versioned, and auditable—no custom connectors per model, no vendor lock-in surprises.

The Multi-Agent Advantage

Many of the upgrades are made with an eye toward agentic use cases, particularly scenarios in which a lead agent commands a group of sub-agents. Managing those tasks requires a strong command of working memory . With standardized skills, you can orchestrate complex workflows across multiple agents—a Sonnet 4.5 orchestrator directing Haiku sub-agents for latency-sensitive tasks—all using the same skill interface.

Why This Matters Right Now

As AI moves beyond chatbots and toward systems that can take actions, the Linux Foundation is launching a new group dedicated to keeping AI agents from splintering into a mess of incompatible, locked-down products. The group, dubbed the Agentic AI Foundation (AAIF), will act as a neutral home for open source projects related to AI agents . Other members in the AAIF include AWS, Bloomberg, Cloudflare, and Google, signaling an industry-level push for shared guardrails so that AI agents can be trustworthy at scale .

Translation: You’re not betting on a single vendor’s whims. Your skills will work across models, clouds, and runtimes.

The Cost and Performance Story

Claude Opus 4.5 is priced at $5 per million input tokens and $25 per million output tokens—a dramatic reduction from the $15 and $75 rates for its predecessor . Pair this with Claude Haiku 4.5, offering similar performance to Sonnet 4 “at one-third the cost and more than twice the speed” , and you have a cost-efficient tiering strategy for multi-agent systems on Azure.

For a typical enterprise workflow—orchestrator handling planning, sub-agents executing tasks—you’re looking at meaningful cost savings while maintaining governance and observability.

What’s Next for .NET Developers

Microsoft has officially launched Visual Studio 2026, marking what they call the first ‘AI-native’ release of its flagship integrated development environment. The general availability rollout follows extensive validation via the Insiders channel and reflects a blend of performance optimisations, deep GitHub Copilot integration, and tooling updates across core languages and workloads . AI-focused capabilities expand through the Microsoft Agent Framework, Microsoft.Extensions.AI, and first-class Model Context Protocol (MCP) support. These systems aim to standardize patterns for building agentic workflows, integrating external tools, enabling telemetry, and working across multiple AI providers .

The practical takeaway: Start building skills today using the Microsoft Agent Framework. They’ll be portable, auditable, and future-proof.

Agent Skills Are Quietly Becoming the Industry's AI Plumbing—Here's What .NET Developers Need to Know