Zone level heating and cooling systems, lighting and blinds in offices have been either controlled by occupants or automated based on fixed setpoints and schedules. Occupants' control of these systems target maintaining a comfortable indoor climate with minimum number of control actions with little intention to save energy. This leads to the suboptimal utilization of daylight and passive solar heat gains. However, when they are automated to mitigate the extravagance in occupant behaviour, operators tend to choose conservative setpoints and schedules that maintain the comfort of the majority and minimize the frequency of complaints with little consideration to save energy again. Although we have been treating the building controllers as our servants maintaining fixed setpoints and schedules, they represent great potential to implement distributed artificial intelligence. To this end, within the scope of this thesis, an adaptive indoor climate control tool was developed. The tool contains novel algorithms that recursively learn from the occupancy patterns, adaptive occupant behaviours, and develop an inverse model of the heat transfer problem in each zone. The algorithms were designed to be embedded inside local building controllers to undertake the learning process in real-time using a small number of low-cost building sensors. The information derived from the occupants and the building’s temperature response was used to autonomously choose operating setpoints and schedules — tailored to exploit the nuances amongst subspaces in a building. The algorithms were implemented and tested for over a year in a controls laboratory which was a shared-office space with a standalone controls network. Furthermore, they were implemented and tested in private offices for over a year. Energy and daylighting simulations were conducted to analyze alternative scenarios regarding the design of these algorithms. Implementation results indicate that the use of adaptive indoor climate control algorithms developed in this thesis can substantially reduce the space heating, cooling, and lighting loads in office buildings — without adversely affecting the occupant comfort. The simulation results indicate that adaptive control of the indoor climate not only reduces the expected load intensities but also reduces the risk of poor operational decisions leading to discomfort and excessive energy use.