This paper introduces a Smart Agent-Based Modelling (SABM) within computational antitrust framework to simulate and detect conditions fostering algorithmic collusion. Using SABM, we document how Large Language Model (LLM)-driven agents achieve tacit collusion in a Bertrand duopoly, stabilizing prices above competitive levels without being explicitly instructed to do so. Simulations in English and Portuguese reveal that linguistic context influences outcomes, and communication between agents potentializes emergent behaviors, such as mimicking concerns about collusion. These findings highlight SABM’s potential to enhance regulatory oversight, offering an accessible tool for antitrust authorities to help address autonomous algorithmic collusion of pricing agents in digital markets.