The article considers an architectural pattern Backend for Frontend (BFF) for developing web systems using microservice approaches. The main purpose of this article is to research aspects that existing solutions like WunderGraph cannot provide and propose a solution that enables client state management using the backend for Frontend pattern architecture specifically tailored for the B2B segment's requirements in Frontend development. During current research, there is defined a concept of API Provider Factory, Public API Gateway, Client State Management and proposes ways for their implementation. Methods of research are based on modeling, analysis, comparing, experiment, and abstracting.

In a delegation problem, a principalP with commitment power tries to pick one out of n options. Each option is drawn independently from a known distribution. Instead of inspecting the options herself, P delegates the information acquisition to a rational and self-interested agentA. After inspection, A proposes one of the options, and P can accept or reject.Delegation is a classic setting in economic information design with many prominent applications, but the computational problems are only poorly understood. In this paper, we study a natural online variant of delegation, in which the agent searches through the options in an online fashion. For each option, he has to irrevocably decide if he wants to propose the current option or discard it, before seeing information on the next option(s). How can we design algorithms for P that approximate the utility of her best option in hindsight?We show that in general P can obtain a Θ(1/n)-approximation and extend this result to ratios of Θ(k/n) in case (1) A has a lookahead of k rounds, or (2) A can propose up to k different options. We provide fine-grained bounds independent of n based on three parameters. If the ratio of maximum and minimum utility for A is bounded by a factor α, we obtain an Ω(log⁡log⁡α/log⁡α)-approximation algorithm, and we show that this is best possible. Additionally, if P cannot distinguish options with the same value for herself, we show that ratios polynomial in 1/α cannot be avoided. If there are at most β different utility values for A, we show a Θ(1/β)-approximation. If the utilities of P and A for each option are related by a factor γ, we obtain an Ω(1/log⁡γ)-approximation, where O(log⁡log⁡γ/log⁡γ) is best possible.

In this paper, we perform a joint design of goal legibility and recognition in a cooperative, multi-agent pathfinding setting with partial observability. More specifically, we consider a set of identical agents (the actors) that move in an environment only partially observable to an observer in the loop. The actors are tasked with reaching a set of locations that need to be serviced in a timely fashion. The observer monitors the actors' behavior from a distance and needs to identify each actor's destination based on the actor's observable movements. Our approach generates legible paths for the actors; namely, it constructs one path from the origin to each destination so that these paths overlap as little as possible while satisfying budget constraints. It also equips the observer with a goal-recognition mapping between unique sequences of observations and destinations, ensuring that the observer can infer an actor's destination by making the minimum number of observations (legibility delay). Our method substantially extends previous work, which is limited to an observer with full observability, showing that optimizing pathfinding for goal legibility and recognition can be performed via a reformulation into a classical minimum cost flow problem in the partially observable case when the algorithms for the fully observable case are appropriately modified. Our empirical evaluation shows that our techniques are as effective in partially observable settings as in fully observable ones.