Plan, Generate, Check, Adjust: A Feedback Loop for AI-Assisted Development

Working with AI coding agents feels like driving a car with a very eager passenger who keeps grabbing the wheel. The moment you describe a destination, the engine revs and lines of code start flying across the screen. That velocity is exhilarating, but it also hides a subtle danger: without a tight feedback loop, the first reasonable-looking output becomes the path you follow, even when it is not the path you need. After months of building with agents, I have started to rely on a simple rhythm that keeps the human in charge: plan, generate, check, adjust.

The planning phase is where most of the value is created or destroyed. It is tempting to skip straight to generation because the agent seems to understand the prompt, yet an AI cannot fill in the gaps you have not articulated. Planning means understanding the problem deeply, defining why you are solving it, setting clear success criteria, and deciding how you want the solution to look before any code appears. This is not waterfall in disguise; it is the hard thinking that makes the later steps cheap. A vague plan produces a plausible prototype that quietly drifts away from the real requirement, while a precise plan gives the agent constraints that actually help.

Once the plan is solid, generation becomes almost mechanical. You feed the context, the acceptance criteria, and the architectural guardrails into the agent, and let it produce an initial version. The real work, however, starts in the check phase. This is where the loop earns its keep. Checking means validating the generated work against the criteria you defined earlier, looking at correctness, fit with the existing codebase, security, and maintainability. It is also where you decide whether the output solves the right problem or merely a nearby problem that looks similar. The faster this check happens, the less rework you accumulate.

Adjusting is the discipline of correcting course before enthusiasm carries you too far. Sometimes the adjustment is a small refactor; sometimes it is throwing away the generated code and tightening the plan. What matters is that the adjustment is explicit and grounded in evidence from the check phase, not a vague feeling that something is off. This mirrors the old test-driven development cycle, but expanded beyond code to include architecture, product fit, and human judgment. The goal is not to produce the most code; it is to converge on the right code with the fewest wrong turns.

Testing is the natural enforcement mechanism of this loop. AI will write better tests when you give it a clear testing framework, fixtures, and helper functions to create entities, but tests also give the check phase something concrete to evaluate. When an agent knows that its output will be exercised by real assertions, it is more likely to stay within the rails. More importantly, tests make the loop repeatable. Every future change can be run through the same plan-generate-check-adjust cycle with confidence that earlier decisions are still intact.

The framework also scales beyond a single coding session. At the project level, the same loop appears as deciding what to build, building it correctly, and monitoring it after release. At the architectural level, it appears as adding quality gates between agent skills so that planning, coding, review, and comment resolution form one continuous stateful process. Each gate forces a check before the next generation begins. Without those gates, an autonomous agent can happily generate itself into a tangle of half-finished features that no one asked for.

One of the hardest habits to keep is resisting the urge to ship the first acceptable version. AI lowers the cost of producing code so much that “good enough” starts to look like the default. I have noticed my own quality expectations softening when the agent hands me something that mostly works. The adjustment phase is the antidote to that slide. It is the deliberate moment where you ask whether the code matches the standards you would hold yourself to, whether the architecture is clean, and whether the next person who touches it will thank you or curse you.

Speed is not the enemy here. Unchecked speed is. The teams that will get the most out of AI-assisted development are the ones that treat generation as the middle of a loop, not the end of it. They plan with rigor, check with honesty, and adjust without sentimentality. In a world where anyone can produce code, the differentiator will not be who generates the fastest; it will be who closes the feedback loop the tightest.