☀️ AI Morning Minute: Agent Loop
The reason a coding agent keeps trying after the first test fails.
The word “agent” gets used loosely enough that it’s stopped meaning much. But there’s a real distinction between an AI that answers your question and an AI that accomplishes your goal, and it comes down to one mechanism. The agent loop is what turns a model that produces text into a system that gets things done.
What it means
The agent loop is the repeating cycle an AI agent runs through to complete a task: perceive, plan, act, observe. The agent takes in the current state of things, decides what to do next, does it, then looks at what happened as a result. That observation feeds the next pass through the loop. It keeps cycling until the goal is met, it runs out of budget, or it hits something it can’t get past.
A chatbot has no loop. You send a prompt, it generates a response, the interaction ends. Whatever it produced is final. An agent treats its own output as an input to the next step. Ask an agent to fix a failing test, and it reads the code, picks a file to check, makes an edit, runs the test, sees the new error, and uses that error to choose its next move. Each iteration is informed by what the last one actually did, not by what the model predicted would happen.
Why it matters
Every agentic product you’ve heard about runs some version of this cycle. Coding agents that submit pull requests, research agents that browse and synthesize, customer service agents that pull records and issue refunds. When a vendor calls their tool “agentic,” the loop is what they mean, whether they say so or not.
The observe step is where things break. An agent that misreads what its last action actually produced can loop forever, repeating a failed approach with slight variations. Anthropic engineers named one version of this the Ralph Wiggum Loop. Reading the result correctly turns out to be harder than taking the action.
Agents need guardrails that chatbots don’t, and the loop is why. When a chatbot hallucinates, you get a bad sentence and you can ignore it. When an agent hallucinates, it takes a bad action, feeds that result into its next decision, and builds from there. Sandboxing, permission layers, and human review checkpoints exist to interrupt that chain before it gets far.
Simple example
You send someone to the grocery store with a list. A chatbot version of that errand hands you back a written plan for how to shop: which aisles, what order, estimated total. Useful, but you still have to go.
The agent version walks in, finds the pasta aisle, sees the brand you wanted is out of stock, checks the shelf tag for a substitute, grabs it, and moves on to the next item. It also might grab the wrong substitute, decide that’s fine, and build the rest of the trip around that call. You find out at checkout.