Claude Code Loops: Why Agentic Build Loops Are Quietly Rewriting How Software Gets Made
The latest trend in AI coding is loops. Here's my take on it.
A loop is an agent that gathers context, decides what to do, acts, checks its own work, and repeats until the job is actually done. It isn't a smarter autocomplete and it isn't a chat window you paste errors into. It's a system that owns its own cycle of work, and that distinction turns out to matter more than almost anything else about how AI is changing software.
At Forest Digital we build most of our internal apps this way now, and increasingly our client work too. This post explains what an agentic build loop actually is, shows you one running step by step, and covers how to run it without it going off the rails. It's written for technical decision makers, with enough depth to be useful but no assumption that you spend your day in a terminal.
The one-shot mental model is the problem
When people are disappointed by AI coding tools, it's usually because they're using a chat model for a job that needs an agent. A chat model answers once. You give it a prompt, it produces code, and the moment it's wrong you're back to babysitting: re-prompting, pasting errors, explaining what broke. The human is the loop. You are the thing carrying state from one step to the next, and that's exhausting and slow.
An agentic loop inverts this. Instead of the human shuttling context back and forth, the agent runs its own cycle. Claude Code, for example, works through three blended phases on any task: gather context, take action, verify results. It searches the codebase to understand what's there, edits files to make changes, runs tests to check whether the change worked, and crucially decides what to do next based on what it just learned. The result of each step becomes the evidence for the next one. The whole transaction is context, decide, act, result, context, repeated until the evidence is strong enough that the task is done (Claude Code Docs).
That last clause is the whole game. "Until the evidence is strong enough" means the agent isn't producing a plausible looking answer and stopping. It's producing a verified one: code that compiles, tests that pass, a behavior it confirmed by running it.
Anatomy of a loop
It helps to make the cycle concrete. A single iteration of an agentic build loop looks like this:
- Observe. Read the relevant files, the test output, the error message, the ticket. Build a current picture of the world.
- Decide. Given the goal and the current state, choose the single next action most likely to make progress.
- Act. Execute it: edit a file, run a command, run the test suite, query a schema.
- Verify. Look at what actually happened. Did the test pass? Did the build break? Is the output what we expected?
- Repeat or stop. If the goal isn't met, feed the new evidence back in and go again. If it is met, and verified, stop.
A simple question to the agent might only touch step one. A bug fix cycles through all five steps several times. A refactor leans heavily on verification. The loop adapts its own length to the difficulty of the task, which is exactly what you want and exactly what a one-shot prompt can never do (Claude API Docs: How the agent loop works).
What a loop actually looks like
Theory is cheap, so here is a real shaped example. Suppose the task is: "Checkout fails when a discount code is applied to an empty cart. Fix it." Here is the loop running, iteration by iteration.
The instruction to the agent:
Fix the bug where applying a discount code to an empty cart throws an error. The checkout test suite must pass. Do not change unrelated behavior.
Iteration 1, observe and decide.
> Searching for the discount logic...
read src/checkout/discount.js
read src/checkout/cart.js
run npm test -- checkout
FAIL checkout/discount.test.js
✕ applies discount to populated cart (passes)
✕ rejects discount on empty cart TypeError: Cannot read
properties of undefined
(reading 'total')
The agent now has evidence, not a guess. The crash is a TypeError on total, and there is already a test describing the behavior it should produce.
Iteration 2, act.
edit src/checkout/discount.js
function applyDiscount(cart, code) {
+ if (!cart.items || cart.items.length === 0) {
+ return { ok: false, reason: 'EMPTY_CART' };
+ }
const total = cart.total;
...
Iteration 3, verify.
run npm test -- checkout
PASS checkout/discount.test.js
✓ applies discount to populated cart
✓ rejects discount on empty cart
The targeted test passes. But the agent doesn't stop yet, because "strong enough evidence" means the whole suite, not just the one case.
Iteration 4, widen the check.
run npm test
FAIL checkout/totals.test.js
✕ recalculates total after item removal
The fix introduced a regression somewhere else. A one-shot tool would have handed you the first patch and declared success. The loop caught it, because verification was real.
Iteration 5, correct and re-verify.
edit src/checkout/cart.js (guard total recalculation)
run npm test
PASS 18 passed, 0 failed
Now the stopping condition is met: the targeted bug is fixed, the full suite is green, and nothing unrelated changed. The agent stops and shows you the diff to review.
Notice what happened. The human wrote one clear instruction with a checkable definition of success. The agent did five cycles of work, including catching and fixing a regression it caused, without a single round trip to the human. That is the difference between an assistant and a loop.
Why this changes the economics, not just the ergonomics
It's tempting to file this under "developer productivity" and move on. That undersells it. Agentic loops change three things that show up directly on a budget.
They move the human up the value chain. When the agent owns the inner loop of write, run, read the error, fix, re-run, the engineer stops being a typist and becomes a reviewer and director. One senior person can supervise work that previously needed a small team, because they're spending their attention on intent and correctness rather than syntax and plumbing.
They compress the feedback cycle. The expensive part of software has never been writing the first version. It's the dozens of small correction cycles afterward, exactly like the regression in the example above. An agent that runs its own tests and reads its own errors collapses a cycle that used to take a developer minutes of context switching into seconds of autonomous iteration. Multiply that across a project and the timeline genuinely shifts.
They make small, well specified work nearly free. A whole category of tasks, such as wiring an integration, scaffolding a CRUD app, adding a provisioning script, or writing the boring 80% of a feature, becomes something you delegate rather than schedule. This is why we aim for our in-house apps to be agent built and agent driven: the marginal cost of "build the obvious thing correctly" drops far enough that you build more of it.
None of this means engineers go away. It means the scarce resource shifts from "people who can write the code" to "people who can specify the right thing and verify it was built correctly." That's a more valuable skill, not a less valuable one.
The hard part isn't starting the loop, it's stopping it well
Here's the honest part, the bit the breathless demos skip. An agentic loop is only as good as its verification and its stopping criteria. A loop with weak verification doesn't fail loudly. It fails confidently. It declares victory on code that looks right and isn't.
Three things separate a loop you can trust from one you can't.
Verification has to be real and automated. "The agent says it's done" is not verification. A passing test suite is. A type checker is. A linter, a successful build, a smoke test against a running instance, these are. In the example above, the entire reason the regression got caught was that success was defined as the full suite passing, not the single fix. The best results come from giving the agent a clear, machine checkable definition of success and letting it grind against that target (MindStudio: Verification, Cost, and Stopping Criteria).
You need explicit stopping criteria. Left undefined, a loop can spin: over-engineering, chasing a flaky test, or burning tokens on a problem it can't solve. Good practice is to bound it. Stop when tests pass, stop after a set number of attempts, stop and ask a human when it hits a decision it shouldn't make alone. Cost is real here too. Every iteration consumes tokens, and a loop without a budget is a loop that can surprise you on the invoice.
The human stays in the loop by design, not by accident. The most productive pattern isn't "fire and forget." It's "plan together, then let it run, then review." You stay able to interrupt at any point to redirect, add context, or tell it to try a different approach. The agent works autonomously but stays responsive to steering (Claude Code Docs). The teams that get burned are the ones that treat the agent as infallible. The teams that win treat it as a fast, tireless junior that needs clear specs and a real review.
How we actually use it
Our pattern at Forest Digital is deliberately unglamorous, because unglamorous is what scales.
We start by writing the spec and the plan with the agent before any code is written. Getting the agent to articulate its plan surfaces misunderstandings while they're cheap to fix. We invest in tests and machine checkable success criteria up front, because that's what lets the loop self-verify rather than self-congratulate. We keep loops bounded, with clear stopping conditions and a human checkpoint at the decisions that carry risk. And we review the output like we'd review a colleague's pull request, because that's exactly what it is.
We pair Claude Code with the rest of our stack, including low-code and no-code tools like Bubble.io, n8n, and others, so the agent handles the dense engineering work while the surrounding workflow stays fast to assemble. The combination is what lets us deliver heavily customized software at a price and pace that traditional development can't match.
The takeaway
The shift from prompts to loops is the difference between an assistant that hands you a draft and a colleague that does the work and shows you the verified result. The technology to run agentic build loops is here and it's good. The differentiator now isn't access to the model, because everyone has that. It's the engineering discipline around the loop: real verification, sane stopping criteria, and a human who knows when to direct and when to get out of the way.
That discipline is most of what separates teams who got a fun demo from teams who actually ship. It's the part we've spent the last year getting right, and it's the part we help our clients get right too.
Forest Digital builds agent-driven software and helps companies adopt AI-driven development that actually ships. If you're weighing how to bring agentic loops into your own delivery, get in touch.