Something is changing in how the strongest engineering teams use Claude Code, the agentic coding tool from Anthropic. The shift is away from typing a fresh instruction every time you want help, and toward building small programs that run the agent for you. The people closest to Claude Code now describe their work as writing loops rather than writing prompts. The agent still does the coding. The human designs the system that keeps it pointed at the right problem.
For Australian teams weighing how much to trust an AI coding agent, this is a useful frame. A loop is not magic and it is not a gimmick. It is a structured way of letting Claude Code work on a task over many steps, with clear signals for when it is doing well and when it should stop. Get the structure right and you win real hours back each week. Get it wrong and a runaway agent can repeat a small mistake across dozens of commits before anyone notices.
Writing loops, not prompts
A one-off prompt is a single request: fix this bug, write this test, refactor this function. You read the result, decide what to do next, and type again. A loop hands that repeated decision back to the system. You define a goal, a way to check progress, and a rule for stopping, then let Claude Code iterate until the goal is met or the limit is reached.
Claude Code now ships a native loops command built for this. The difference from wrapping the command-line tool in a shell script matters more than it first appears. A shell script cold-starts the agent on every call, throwing away context each time. A native loop runs inside a persistent session, so the context window, the tool permissions, and any connected services carry across each iteration. The agent remembers what it tried last round, which is the entire point.
A loop is not a cron job
The easiest mistake is to treat a loop as a timer that re-runs the same instruction. A real loop needs three things a cron job does not have. Without them, repetition turns small errors into large ones.
A feedback signal: tests, type checks, a build result, or a real error message. The agent needs something that can tell it an attempt failed, so it corrects course rather than drifts.
A stop condition: a clear rule for when to finish, roll back, or hand the work to a person. An agent with no exit will keep going long after it has stopped being useful.
Bounded scope: a narrow, well-defined task. The tighter the boundary, the less room there is for the agent to wander into changes nobody asked for.
A practical example looks like this: every five minutes, check whether the pull request build passed; if it failed, read the error log, fix the cause, and push a new commit. The build result is the feedback signal. A limit of a few hours is the stop condition. One pull request is the bounded scope. That is a loop a team can actually trust to run unattended.
The cost reality to plan for
Every iteration of a loop is another model call, and the bill follows the iterations. A loop that checks something once a minute for eight hours is roughly 480 calls. Run several of those across a team and the token spend climbs quickly. This is the part that catches people out: the productivity gain is real, but so is the cost line, and the two need to be managed together.
For a small Australian development team, a sensible monthly budget for agent usage often lands between $2,000 and $8,000, depending on how many loops run and how tightly they are scoped. A single poorly bounded loop left running overnight can add a few hundred dollars on its own. Against the cost of an engineer's time, that is usually money well spent, but only when the loops do useful work rather than spin. A tighter loop will often reach the same result more slowly and at a fraction of the cost, which is the right trade for a team watching its spend.
Set a token or time budget per loop before you start, not after the invoice arrives.
Prefer a slightly slower, tightly bounded loop over a fast one with no limit.
Track spend per project so a single runaway loop is easy to spot.
Guardrails before autonomy
The temptation with a capable agent is to hand it more and more rope. The discipline that separates a useful setup from a risky one is putting guardrails in first. In a regulated Australian context, that discipline is not optional. A loop that touches customer data or production systems needs the same review and logging you would apply to any automated process, and your Privacy Act obligations do not pause because an agent did the work rather than a person.
Practical guardrails are not complicated. Keep loops on a branch, never straight to main. Require a human to approve any merge. Give the agent wide read access but narrow write access. Log every action so you can reconstruct what happened if a loop misbehaves. None of this slows a good team down. It is what lets them run agents with confidence rather than crossing their fingers.
Where to start
The best first loop is a small, low-risk one with an obvious feedback signal. Keeping a test suite green, clearing lint errors, or chasing down a flaky build are all good candidates. They have a clear definition of done, they are easy to bound, and a mistake is cheap to undo. Prove the pattern on something safe, measure the time saved, then widen the scope from a base you trust.
Writing loops instead of prompts is less a new feature than a new habit. The Australian teams getting the most from Claude Code are the ones who treat the agent as a system to be designed, with signals and limits, rather than a chat box to be poked. Book a brainstorm and we will help you set up your first safe, well-bounded loops with sensible cost controls around them, mapped to the work your team actually does.
