The practice of designing and tuning the iterative feedback cycles in agentic AI systems, where each cycle consists of a generation step, an evaluation step, and a revision step, repeated until output meets a defined quality threshold or a stop condition is reached.
Also known as agentic loop design, eval-revise loop, generation loop optimization
Loop engineering is the craft of designing the iterative cycles that drive agentic AI workflows. In a basic agentic loop, an AI model generates an output, evaluates that output against some criteria, and revises it based on what it found. The loop runs again with the revised output, and continues until either the output passes the evaluation or a maximum iteration count is reached. Loop engineering is the work of deciding what each of those steps contains, how they connect, and when the loop stops.
The components a loop engineer controls are: the generation step (what prompt drives each attempt, what context is passed, what model is used), the evaluation step (what criteria are applied, whether evaluation is done by the same model, a different model, a scoring function, or a human checkpoint), the revision step (how evaluation feedback is fed back into the next generation attempt), and the stop conditions (a passing score threshold, a maximum iteration count, a timeout, or a human approval gate). Getting these components right is the difference between a loop that reliably improves output quality and one that wastes compute cycling through bad iterations or over-refining past the point of diminishing returns.
Loop engineering sits at the intersection of prompt engineering and systems design. A poorly engineered loop can produce output that degrades with each iteration as the model gets confused by accumulating context, or that never exits because the stop condition is set too high, or that exits too early because the evaluation criteria are too easy to pass. A well-engineered loop uses the minimum number of iterations to produce reliably acceptable output, with clear exit conditions and predictable compute cost per run.
Most of the AI workflows that matter in an agency involve iteration: generating copy and refining it against brand criteria, drafting a brief and checking it against a client framework, producing a media plan and validating it against budget constraints. Whether that iteration is explicit (a designed loop) or implicit (the human manually prompting revisions), the loop is always there. Loop engineering makes that iteration deliberate, measurable, and cost-controlled rather than ad hoc and open-ended.
The compute cost dimension is real. Each iteration in a loop costs tokens. A workflow that runs 10 iterations costs 10 times as much as one that runs 1. Agencies building production AI workflows on volume, whether that’s generating ad copy variants, processing creative briefs, or classifying assets, need to design their loops to hit acceptable quality in the minimum number of iterations. An untuned loop that runs 8 iterations when 3 would suffice is burning money on every run.
The quality ceiling is a design choice. The evaluation criteria in the loop determine what “good enough” means. Agencies that define those criteria carefully (does this copy pass brand voice? does this brief contain all required sections? does this asset match the creative spec?) get consistent, auditable output. Agencies that skip the evaluation step get a single-pass output that may or may not meet the standard, with no mechanism for self-correction.
Human-in-the-loop placement matters. Loop engineering includes deciding where humans enter the cycle and where they don’t. A loop that requires human approval on every iteration defeats the purpose of the loop. A loop that has no human checkpoint produces output that no one has reviewed before it reaches the client. The right design places human review at the exit of the loop, not inside it, unless the task genuinely requires human judgment to evaluate mid-cycle.
A creative agency is building a workflow to generate social ad copy variants from a creative brief. The first version of the workflow generates copy and passes it straight to the human reviewer. The reviewer spends most of the review rejecting copy that doesn’t match the client’s brand voice, which defeats the point of using AI. The agency redesigns the workflow as a loop: the model generates a copy variant, a second model evaluates it against a stored brand voice rubric and returns a pass/fail with notes, and if the copy fails, the notes are fed back to the generating model for a revised attempt. The loop exits on a pass or after three iterations, whichever comes first. After tuning the rubric and the revision prompt, 80 percent of copy variants pass within two iterations. The human reviewer now sees copy that has already cleared the brand voice check, and review time drops from 40 minutes per batch to 12 minutes. The per-batch compute cost is predictable because the loop is bounded.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.