Agentic Coding Is About Closed Loops, Not Prompts
Most people who struggle with agentic coding are solving the wrong problem.
They spend time crafting better prompts, choosing better models, or chaining more tools together. And then they wonder why the output still feels chaotic — why the agent goes off in three directions at once, produces something vaguely plausible, and leaves them cleaning up the mess.
The problem usually isn’t the prompt. It’s the loop.
Open loops fail
When you tell an agent “build me a tax calculation module,” you’ve created an open loop. The agent produces something. You look at it. You decide if it’s good. You try again. The feedback — your judgment — lives entirely outside the system.
Open loops are exhausting. Every iteration requires you to context-switch, evaluate, and re-engage. More importantly, the agent has no way to know whether it’s succeeding. It’s generating in the dark.
This is why agentic coding, done carelessly, often produces more work than it saves. You end up reviewing more code than you would have written.
What a closed loop actually looks like
A closed loop has three components:
1. A defined scope — not “build X,” but “build X, ending here, touching only these parts”
2. A clear expected output — something the agent can verify against, not just aim for
3. A feedback signal — test results, lint output, type errors, a diff, anything that tells the agent whether it’s closer or further from done
With these three things in place, the agent can iterate on its own. It writes code, runs the tests, sees what breaks, fixes it, runs again. You’re not in the loop until it’s finished — or until it gets genuinely stuck.
The difference in output quality is not marginal. It’s large.
The hard part is the scope
Here’s the thing nobody tells you: defining a good scope is difficult. It requires knowing where to cut.
A scope that’s too large brings the loop back open — there are too many moving parts for feedback signals to be meaningful. A scope that’s too small produces fragments that don’t compose.
Getting this right requires development experience. You need to know what a reasonable unit of work looks like. You need to understand the boundaries of your codebase well enough to draw a fence around a specific piece of it.
This is why agentic coding tends to work better for experienced developers than for beginners — and why the common advice to “just let the AI do it” often backfires. Without the ability to define scope precisely, you’re handing the agent an open field and hoping it stays in bounds.
Skill is in the design, not the prompt
The implication is worth sitting with: the bottleneck in agentic coding isn’t prompting ability. It’s systems thinking.
The developers who get the most out of agentic tools are the ones who can decompose problems clearly, define interfaces before implementation, and specify what done looks like before they start. In other words, they’re good software engineers first.
Agentic coding doesn’t lower the bar for good engineering judgment. If anything, it raises the stakes — because a poorly scoped task, handed to an agent that iterates quickly, will confidently build the wrong thing at speed.
Define the loop first. Then let the agent run.
Written based on what I’ve found actually works — building tax-tech products where correctness isn’t optional.