AI & development glossary

An AI agent is a software system capable of pursuing goals through autonomous decision making and environment interaction. Unlike a standard AI assistant that merely processes input, an agentic AI utilizes reasoning loops, tool usage, and memory to execute complex multi-step workflows with minimal human oversight to complete specific business objectives.

Claude skills refer to the functional capabilities and tool-use mechanisms that enable the Anthropic AI model to interact with external environments. These skills allow Claude to execute specific operations like code execution, data retrieval, and API integration, effectively transforming the model into an autonomous agent capable of solving complex technical tasks independently.

Computer use AI is a category of autonomous software agents designed to operate desktop or web interfaces by processing visual inputs and executing keyboard and mouse commands. These systems mimic human interaction with operating systems to automate complex workflows like browser navigation, data entry, and multi-app software orchestration through natural language.

Generative engine optimization is a search marketing discipline that optimizes content for artificial intelligence platforms and answer engines. It focuses on LLM citation and high-quality data structuring to ensure brands appear as authoritative sources within generated responses, rather than relying solely on traditional link-based rankings found in standard search engine results pages.

Genkit is an open source AI framework developed by Google that enables developers to build, deploy, and monitor production ready AI applications. It integrates seamlessly with Firebase AI and Google Cloud, providing tools for model abstraction, workflow orchestration, and prompt management to streamline the development lifecycle for modern large language model applications.

llms.txt is a standardized plain text file that website owners place on their servers to communicate usage policies and content availability to AI crawlers. Acting like a robots.txt for large language models, this GEO standard allows developers to specify which data sets are available for model training and retrieval applications.

Multi-agent orchestration is a framework that governs the interactions, task delegation, and communication protocols between autonomous AI agents. It enables complex workflows by allowing specialized agents to collaborate within an agent swarm, using coordination logic to solve multi-step problems that exceed the capabilities of a single large language model deployment.

n8n is a fair-code workflow automation tool that enables users to integrate various applications and services through visual nodes. Designed for technical users and developers, it supports both self-hosted automation and cloud deployments, providing granular control over data processing and no-code automation tasks without the restrictions often found in closed proprietary platforms.

Prompt caching is a technique that stores frequently used input tokens in memory to accelerate inference and enable significant llm cost reduction. By reusing context from previous requests, models avoid redundant processing of static prompts, which lowers latency and improves operational efficiency for systems using anthropic prompt caching or openai prompt caching strategies.

RAG is a framework that connects large language models to external data sources to improve response accuracy. By combining retrieval-augmented generation with vector search, the system identifies relevant information and uses it as grounding for the model. This process reduces hallucinations by ensuring outputs are based on verified, proprietary business datasets.

A reasoning model is an artificial intelligence architecture designed to perform extended thinking before providing a response. By employing chain of thought processing, these systems break down complex problems into logical steps. Examples like OpenAI o1, o3, and DeepSeek R1 demonstrate improved performance on multi-step reasoning tasks compared to standard language models.

Tool use is the capability of large language models to interact with external software or APIs to perform specific tasks. Through structured tool calling, models generate requests to external functions, receive dynamic data, and incorporate those results into their responses, effectively overcoming the limitations of static training data and standard natural language processing.

Vibe coding is an AI-assisted development methodology where software engineers prioritize natural language prompting over traditional syntax to define application requirements. By leveraging tools like Claude code, Cursor, and Windsurf, developers focus on high-level architecture and feature iteration, allowing large language models to generate, interpret, and refactor the underlying source code.

A background agent is an autonomous software process designed to execute complex, long-running tasks independently of the primary user interface. By functioning as an async agent, it performs asynchronous AI tasks such as data processing or research in the background, allowing systems to maintain performance while managing resource-heavy operations without blocking user interaction.

An MCP server is a standardized communication interface that connects AI models to external data sources and developer tools using the Model Context Protocol. By providing a unified framework for data integration, it allows LLMs to interact with local databases, file systems, and internal APIs without requiring bespoke integrations for every new application.