Why 'Loop Engineering' in AI Works Now but Struggled in 2022–23

Loop engineering — where an AI agent repeatedly generates, tests, and refines its output until a problem is solved — is not a new concept, but earlier language models lacked the consistency and context capacity to make it practical. Older models frequently misunderstood feedback, repeated mistakes, and lost track of prior attempts as limited context windows forced older messages to be dropped. Advances in three key areas have changed this: larger context windows let models retain full iteration histories, improved consistency means models now converge on correct solutions rather than producing varied half-baked answers, and better tool integration allows models to actually run code and read real error outputs. Falling inference costs have also made running multiple back-to-back iterations economically viable, whereas in 2022–23 the compute expense discouraged casual looping. Tools like Claude Code automate this generate–verify–retry cycle, but the core mechanic remains a simple feedback loop that any user can replicate manually with any capable model.