Honeypots are deception systems used to emulate vulnerable services and collect threat intelligence. In constrained environments, where network or computational resources limit the number of deployable honeypots, security experts typically decide which assets to expose statically or semi-automatically. Attackers’ tactics change quickly, and existing rule-based deception systems cannot cope with this dynamism, reducing their ability to capture valuable information about adversarial behavior. Dynamic deception architectures, if properly tuned, can autonomously collect high-value threat intelligence without constant human supervision. This thesis addresses the lack of adaptivity and autonomy in current honeypot systems, investigating whether an AI-driven agentic architecture can autonomously manage honeypot exposure to maximize information gain from attackers.