The agent loop, live
Every AI agent runs the same four-step cycle: think, act, observe, repeat, until the goal is met or a rule stops it. Press play to watch it run on a real task — then switch to the second scenario to see what happens when a loop gets stuck, and why guardrails matter.
Goal: Fix the one failing test in this project.
…
…
…
What you just watched
In the first scenario the loop converges: each pass reads the real result of the last action (the failing tests), reasons about it, and tries again — until the stop condition (all tests pass) is met. That feedback step, observe, is the whole point. It is what lets an agent use tools and correct its own mistakes, which a single prompt cannot do.
In the second scenario the loop gets stuck repeating a failed action. Without a guardrail it would spin forever, burning time and cost. The no-progress rule catches it — after three attempts with no change, the agent stops and asks for help instead. That is a good failure, and it is exactly the kind of guardrail (alongside a step limit and a clear stop condition) that turns a risky loop into a dependable one.
Want the full picture?
This demo is the core idea from our free, plain-language course on agentic AI — the loop, the guardrails, and how to tell when a task needs an agent at all.