LLM-driven agents

We keep hearing about large language model (LLM) agents. They’re not magic. They’re programs that take a big goal, break it down, and keep prompting themselves until they’re done. The trick is less about smarts, more about persistence.

Goals

An LLM-driven agent starts with one high-level instruction. “Plan a weekend trip.” “Write a marketing brief.” She doesn’t panic. She breaks the work into smaller steps. Each step is just another prompt. Then she keeps going until the job looks complete. The secret isn’t brains; it’s that she doesn’t stop when we would.

Goal Decomposition

Humans do this without thinking. If we say “cook dinner,” we know we’ll need groceries, recipes, and a stove. An agent spells it out. Step 1: find a recipe. Step 2: make a shopping list. Step 3: buy groceries. Goal decomposition is her way of staying on track. No assumptions. Just steps.

Self Prompting

Normally, we write the prompts. With agents, she writes her own. That’s the self-prompting trick. After each output, she asks herself, “What’s next?” and answers with another prompt. It’s like watching someone talk to herself, but she doesn’t forget what she said three sentences ago.

AutoGPT

AutoGPT is the poster child. It strings prompts together, checks the results, and keeps pushing forward. It’s clunky at times. She wanders off or loops on useless steps. Still, it shows the idea: give an AI a goal and let her chase it down without us babysitting.

Where It Leaves Us

We wanted software that follows orders. We got something closer to an intern who won’t quit until she thinks she’s finished. She’s messy, she overshoots, but she teaches us what’s possible. Our job is to decide when to step in and when to let her run.