Running LLMs in production is not like running a traditional API. The failure modes are different, the metrics are different, and the debugging process is different. Here is how to build observability for AI systems that actually tells you something useful.

MCP gives your AI application a clean integration layer. Here is how to architect a production application that uses MCP servers effectively — from server selection and composition to state management and deployment.

When an AI model can call tools, read files, and take actions, the UI needs to expose that activity clearly. MCP introduces specific UX challenges around trust, transparency, and control that generic chat interfaces were not designed for.

Model Context Protocol standardizes how AI models connect to external tools and data. Instead of writing custom integrations for every LLM application, you build one MCP server and any compliant host can use it.

Tools are what turn an LLM into an agent. Understanding how function calling works at the API level — and how to compose agents as tools for other agents — is foundational to building reliable AI systems.

AI agents are not magic — they are LLMs in a loop with access to tools. Understanding the different patterns, from simple ReAct agents to multi-agent networks with A2A communication, helps you pick the right architecture for the job.

The quality of an LLM's output is bounded by the quality of its context. Context engineering is the practice of deciding precisely what information to include, how to structure it, and when to retrieve or compress it.

Shipping an LLM feature without an evaluation framework is guessing. Here is how to build a systematic approach to measuring output quality — before problems reach production.

Prompt engineering is not about finding magic words. It is about giving the model the context and structure it needs to do the job reliably — every time, not just when you test it.

Most LLM projects stall not because the model fails, but because teams underestimate the operational work. Here's what production deployment actually looks like across healthcare, finance, and software engineering.