AI in Production: The Week Pragmatism Won (January 2026 Roundup)

TL;DR 2026 marks AI’s shift from hype to pragmatism, with focus moving away from ever-larger language models toward making AI usable. Microsoft moved Model Context Protocol (MCP) support for Azure Functions to General Availability, solving the security pain point preventing AI agents from accessing sensitive enterprise data. OpenAI released GPT-5.2, described as its “most capable model series yet for professional knowledge work,” while Azure OpenAI enabled Prompt Caching for o1-preview, o1-mini, GPT-4o, and GPT-4o-mini models, allowing developers to optimize costs and latency by reusing recently seen input tokens. For .NET teams: expect smaller, fine-tuned models and agentic workflows to dominate 2026—not flashy demos.

The MCP Moment: Agents Finally Get Real Plumbing

Anthropic’s Model Context Protocol (MCP), a “USB-C for AI” that lets AI agents talk to external tools like databases, search engines, and APIs, proved the missing connective tissue and is quickly becoming the standard. OpenAI and Microsoft have publicly embraced MCP. This week, Microsoft delivered the goods: MCP support for Azure Functions moved to General Availability, now supporting .NET, Java, JavaScript, Python, and TypeScript, with a new self-hosted option letting developers deploy existing MCP SDK-based servers without code changes. What this means for you:
If you’ve been sitting on agentic AI pilots, now’s the time to move. The platform integrates directly with Azure AI Foundry, allowing agents to discover and invoke MCP tools without additional configuration layers. Your .NET agents can now safely call downstream enterprise systems—no more hand-rolled auth nightmares.

// Quickstart: Azure Functions MCP Extension (C# .NET)
// See: https://learn.microsoft.com/en-us/azure/azure-functions/functions-bindings-model-context-protocol
// Coming to a repo near you—stay tuned for full samples

Small Models, Big Returns

AT&T’s chief data officer told TechCrunch that “fine-tuned SLMs will be the big trend and become a staple used by mature AI enterprises in 2026,” noting that if fine-tuned properly, they match larger generalized models in accuracy for enterprise business applications while being superior in cost and speed. This is the real story. In practice, that involves deploying smaller models where they fit, embedding intelligence into physical devices, and designing systems that integrate cleanly into human workflows. For .NET developers: Microsoft launched the Azure OpenAI Service in early 2023, and soon after introduced Semantic Kernel (SK) for orchestrating prompts, memories, and plugins using C# or Python, and Microsoft Extensions for AI (MEAI) for unified abstractions for interacting with models (e.g., IChatClient). Microsoft Foundry is the enterprise platform for model catalogs (OpenAI, Meta, DeepSeek, Cohere, Mistral, etc.), where organizations take AI into production at scale. —

Cost & Latency: The Real Levers

Azure OpenAI Batch API for Global deployments is now generally available, allowing developers to manage large-scale and high-volume processing tasks more efficiently with separate quota, a 24-hour turnaround time, at 50% less cost than Standard Global. Prompt Caching offers a 50% discount on cached input tokens on Standard offering and faster processing times. Azure also lowered the initial deployment quantity for GPT-4o models to 15 Provisioned Throughput Units (PTUs) with additional increments of 5 PTUs, and lowered Provisioned Global Hourly pricing by 50%. Translation: If you’re building chat applications, code editors, or long-context workflows, Prompt Caching is your friend. If you’re batch-processing documents or logs at scale, Batch API cuts your bill in half.

Real Talk: Enterprise AI Still Needs Guardrails

An MIT survey in August found that 95% of enterprises weren’t getting a meaningful return on their investments in AI. But enterprise-focused VCs overwhelmingly think 2026 will be the year when enterprises start to meaningfully adopt AI, see value from it, and increase their budgets for the tech. The catch? Most investors said budget increases will be concentrated and that many enterprises will spend more funds on fewer contracts. 2026 will be the year that enterprises start consolidating their investments and picking winners, as enterprises are testing multiple tools for a single-use case and it’s extremely hard to discern differentiation even during proof of concepts. For your roadmap:
Don’t chase every new model. Be critical when vendors promise “80% accuracy” as if that’s the whole story—this is still generative AI in early 2026. Treat claims as marketing until you’ve seen working results in your own codebase, with your constraints, and your risk profile. —

What’s Next: Your 2026 Checklist

Evaluate MCP for your agents. With MCP reducing the friction of connecting agents to real systems, 2026 is likely to be the year agentic workflows finally move from demos into day-to-day practice.
Prototype with smaller models. Foundry Local is a great option for testing new models, testing new code without blowing through budget, and building CI/CD pipelines with minimal overhead. Ollama is a popular open-source engine for running lightweight and mid-sized models locally.
Optimize costs with Batch API and Prompt Caching. If you’re handling high-volume or repetitive queries, these features pay for themselves.
Invest in observability and evals. Focus on custom models, fine tuning, evals, observability, orchestration, and data sovereignty. —