Agility delivers the most benefit when uncertainty is high—when the problem is fuzzy, assumptions are unproven, stakeholders are still learning, and change is inevitable. In these contexts, iterative and incremental delivery, frequent feedback, and tight collaboration reduce risk by turning unknowns into knowns quickly. Timeboxing, continuous prioritization (e.g., MoSCoW), and demonstrable increments enable evidence-based decisions, allowing the team to pivot as understanding improves. Conversely, when the problem and solution are truly stable and well defined, predictive approaches can work adequately because variability is low. Likewise, heavy upfront research or a fully designed/contracted “optimal” solution implies a desire for certainty that may not exist; agile avoids over-investing in speculative plans by validating value early. Therefore, agility is most needed when discovery is paramount and adaptability is crucial—when the problem is unclear and change is inevitable.