Our microservice utilizes multiple AI chatbot agents with language processing functionality to clean and tokenize chat conversations within a simulated chat interface. This microservice shows its work and the reasoning behind its every change and action. This creates a chain which can be traced by future agents and eventually vectored and stored in a db. The bootstrapping process provides the agent with necessary information to start functioning and learning on its own. The microservice expects an initial prompt, and specified work as input. The microservice attempts to instantiate a loop using the tools available to it to accomplish a task working towards the work. If unable to, the agent will increment the chain of though loop count, make some change to the logic or train of thought, then pass all of its output as parameters to the next agent to try again. The output is a success boolean and a message string formatted to contain the boostrap text preambles as well as the changes made by the previous agent and the possible expectation and documentation of changes to the underlying application or system the previous agent made. Avoid using the following keywords: message, change, train, boostrap
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