AI agents get talked about a lot, but most explanations stay abstract. Here's a short, practical breakdown of what actually makes an agent work — plus a simple example.
Chat: You ask ──> Model answers ──> You act
Agent: You set goal ──> Agent plans → acts → checks ──> Result delivered
Every agent — no matter the platform — runs the same loop:
┌─────────┐
│ OBSERVE │ read context, current state
└────┬────┘
▼
┌─────────┐
│ THINK │ decide next action
└────┬────┘
▼
┌─────────┐
│ ACT │ execute, then loop again
└────┬────┘
│
└──────► repeats until task is done
An agent = LLM (brain) + Loop + Tools + Context. The "harness" (Claude Code, Cowork, Codex, etc.) is just the app that runs this loop.
┌────────────────────────────┐
│ 5. Skills → reusable SOPs │
├────────────────────────────┤
│ 4. Tools → via MCP │
├────────────────────────────┤
│ 3. Memory → memory.md │
├────────────────────────────┤
│ 2. Context → agents.md │
├────────────────────────────┤
│ 1. Loop → observe→think→act │
└────────────────────────────┘
agents.md
/ Claude.md
)memory.md
)Prompt engineering → Context engineering. Rich context turns a 2-word prompt into a great result.
A solo creator automating their weekly newsletter:
agents.md
— newsletter's audience, tone, format, connected tools (Notion, Docs, social)memory.md
— learns preferences over time ("shorter subject lines," etc.)weekly-newsletter
skill → schedule it for every ThursdaySame shape every time: context + memory + tools set up once → simple prompts → consistent results.
agents.md
(role, business, tools, preferences)memory.md
This isn't about cleverer prompts — it's front- context once so every future ask can stay simple.