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We investigate and model the dynamics of two-dimensional stochastic self-assembly of intelligent micro-systems with minimal requirements in terms of sensing, actuation, and control. A microscopic agent-based model accounts for spatiality and serves as a baseline for assessing the accuracy of models at higher abstraction level. Spatiality is relaxed in Monte Carlo simulations, which still capture the binding energy of each individual aggregate. Finally, we introduce a macroscopic model that only keeps track of the average number of aggregates in each energy state. This model is able to quantitatively and qualitatively predict the dynamics observed at lower, more detailed modeling levels. Since we investigate an idealized system, thus making very few assumptions about the exact nature of the final target system, our framework is potentially applicable to a large body of self-assembling agents ranging from functional micro-robots endowed with simple sensors and actuators to elementary microfabricated parts. In particular, we show how our suite of models at different abstraction levels can be used for optimizing both the design of the building blocks and the control of the stochastic process.