Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time. Recent anytime-valid envelopes do, but deterministic ones are typically too conservative for practical stopping, and existing data-adaptive ones are derived case-by-case, each concerned with a specific algorithm and a specific noise model and boundary construction. This paper provides a general recipe for sharp, data-adaptive anytime-valid envelopes for a large class of iterative algorithms. Our approach has two key steps: an optimization module derives a one-step recursion that performance measures of many such algorithms satisfy, reducing the error analysis to time-uniform concentration for predictably weighted sums of noise. Then, a statistical module ports the sub-$ψ$ condition to vector-valued processes, uniformly over predictable weights, and derives off-the-shelf boundaries via mixtures, stitching, and product betting martingales. Combining the two yields readymade envelopes for many iterative algoithms including but not limited to stochastic gradient descent (SGD), Oja's algorithm, temporal difference learning, Q-learning, and Bures-Wasserstein SGD, many with no prior data-adaptive anytime-valid guarantees. Specifically, for SGD, our framework recovers, often under weaker conditions, or significantly sharpens every existing envelope we are aware of. Our constructions come with explicit implementation details and are substantiated by numerical experiments.

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