Early phases of biomedical research require the ability to interact with the biological system in real time in response to frequent refinements of experimental conditions made during the discovery process: Translating these requirements into automation terms means to implement the ability to change experimental parameters based on incoming data with fine control at the individual microtiter plate and even well level in real-time. Optimally, this means implementing the ability for the system to make dynamic adjustments on its own scheduling during the run of an automated experiment based on incoming data. Here we present a prototype of an automated system consisting of a high-level commands that cover setup, automation, handling of an attached SQL database and an integrated executive process (daemon), which together drive a robotic system to perform fully automated experiments while maintaining this required experimental flexibility. The environment is able to take high level experimental/biological requests given in command line format and translates them into high-level automation commands and a schedule for their execution written to a database. The actual execution of the experiments is driven by a daemon iterating over active experiments and parameters in the database while avoiding conflicts on equipment. The same process also surveils consumable resources and processes results and flags noteworthy outcomes while writing the results into the database. In contrast to current real-time or pre-emptive automation schedulers, the database that drives the execution of experiments is directly accessible to the scientist who can extract, analyze and visualize results in e.g. R or Python. Based on the results, the scientist is able to use standard programming syntax (including loops and recursion) within the interpreter to modify the experimental parameters of plates while the actual experiment is still in progress. The scientist can also set up rules or utilize e.g. artificial intelligence algorithms to direct modify parameters and execution. This allows the scientist to adjust the experimental flow in response to biological outcomes obtained in real time while remaining in full control of the all experimental details at any time. A key feature of our system is its full transparency. It is constructed in Python, meaning that the program code is fully auditable, which is vital for security purposes. Also, this setup enables easy implementation of protocols like AES for full encryption of the communication from each peripheral device to system or other storage location and also allows for securing the remote access into the system for scientists working off-site.