AI agents work by running a continuous loop of thinking, acting, and observing—unlike a one-shot chatbot. You give an agent a goal, it plans the next step, calls tools (search, APIs, file reads), sees the results, then repeats until the job’s done. That cycle makes agents ideal for tasks with unpredictable steps, like debugging tests or researching sources. If you just need a fixed output, a prompt or script is cheaper and faster; use agents when you need feedback-driven flexibility. Under the hood, every system shares the same building blocks. State lives partly in the context window (your prompt, instructions, previous tool calls) and partly outside it (files on disk, databases, saved memories). You have to decide where to store what: code and project files belong in Git-tracked files; erratic or session-persistent facts go in memory; structured, multi-user data belongs in a database. As soon as you span multiple agents, common patterns emerge—planner/executor splits thinking from doing, router/specialist dispatches requests to focused mini-agents, and map-reduce breaks big tasks into parallel chunks then aggregates their output. Your agent’s performance hinges more on clear instructions than on the latest LLM. Keep global config files (CLAUDE.md, AGENTS.md) under 100 lines with project-specific rules—package manager, coding style, team conventions. Offload task-specific guides into reusable workflow files that load only when needed: testing guidelines, PR checklists, migration steps. Tests show those targeted instructions on a cheaper model outperform a pricier model saddled with generic prompts. In short, master the core concepts once, and every new “revolutionary” agent framework will feel familiar.