AI Agent
An AI agent is a system that takes a goal, breaks it into steps, and executes those steps autonomously—calling tools, reading results, adjusting course—without a human approving each action. Where a chatbot waits for your next prompt, an agent decides what to do next on its own. The concept borrows from reinforcement learning and robotics, but the current wave runs on large language models that can reason about which tool to use when. Agents are powerful when the task has clear success criteria and bounded risk. They are dangerous when the goal is vague, the environment is unfamiliar, or the cost of a wrong action is high. Most production agent systems today still need tight guardrails and human checkpoints—full autonomy remains more demo than reality.
Related terms:
Generative AI
Generative AI refers to AI systems that learn statistical patterns from training data to create new content—such as text, images, code, audio, or video—rather than classifying or analyzing existing data. This marks a shift from earlier discriminative models like spam filters and recommendation engines, with tools like ChatGPT, DALL-E, Midjourney, and Stable Diffusion driving its rapid mainstream adoption.
Embeddings
Embeddings are numerical representations of text—vectors of hundreds or thousands of floating-point numbers—that capture semantic meaning in a form machines can compare mathematically. They power semantic search, recommendation engines, clustering, anomaly detection, and the retrieval half of RAG architectures.
WWGPTD
WWGPTD began as internal Slack shorthand to remind teams that using AI isn’t cheating but the essential first step. The accompanying bracelets serve to normalize AI as a fundamental tool for creating better work.