Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard introduced by Anthropic in 2024 that defines how AI models connect to external tools and data sources through a unified interface. Before MCP, every integration between a model and an external system—a database, a file system, an API—required custom glue code. MCP standardizes this with a client-server architecture: the AI application is the client, and each data source or tool runs an MCP server that exposes its capabilities in a format any MCP-compatible model can understand. Think of it as USB for AI integrations. The protocol handles tool discovery, authentication, and structured input/output so developers build the connector once and any MCP client can use it. MCP matters because the value of AI systems increasingly depends on what they can access and act on, not just what they know from training.
Related terms:
Prompt Engineering
Prompt engineering involves designing and refining inputs—ranging from simple instructions to detailed system prompts with examples, constraints, personas, and chain-of-thought scaffolding—to elicit desired outputs from a language model. It’s the most accessible way to boost AI performance, requiring no training data or ML expertise, but prompts can be fragile, hard to version-control, and easy to overfit.
Transformer
The transformer is the neural network architecture introduced in Vaswani et al.’s “Attention Is All You Need” that replaces recurrence with parallel self-attention, enabling efficient training on internet-scale data. Its simple, scalable focus on attention powers state-of-the-art models across text, vision, protein folding, audio synthesis, and more.
Agentic AI
Agentic AI refers to systems that autonomously pursue goals—planning actions, employing tools, and adapting based on feedback—without waiting for human instructions at every step. Unlike passive AI that only responds when prompted, agentic AI can monitor systems, diagnose issues, and propose fixes on its own.