Agent-led parallelization

- Some generative tasks are so huge that they can require a great deal of processing power and time to complete. This is no secret, but it's not always practical for businesses that need generated content provided quickly, especially at runtime. For example, here we have a runtime application that needs content generated on demand and then provided within a very short timeframe. However, the generative AI system is overwhelmed by the quantity or complexity of the content it's being asked to produce and ends up taking much longer than the application can wait resulting in a problem. We also show a human in this scenario as timeframes for human interaction can also be important, such as when the human is relying on the generated content for a work task. The agent-led parallelization pattern introduces intelligent agent logic capable of decomposing a greater task into a set of subtasks. It then can delegate the completion of each subtask to a different generative AI system to complete. Once all the subtasks are completed, the agent compiles them together to produce the requested output in a much shorter time. This logic can reside in a central agent or it can be split between an orchestrator and a dedicated autonomous load balancing agent. The type of logic required by the orchestrator agent includes task decomposition logic that enables the agent to intelligently break a single complex task into smaller, independent subtasks. The agent needs to be able to understand which parts of the task can be processed independently and concurrently. The additional workflow logic required to synthesize the individual outputs into a final result would also be part of the agent. If the load balancing logic is within a separate agent, then that agent would need the following types of capabilities. Task ingestion logic that allows the agent to receive and queue incoming subtasks from the orchestrator, distribution logic, which is the core logic that decides how to efficiently distribute the queued subtasks to different generative AI systems or servers. It uses a load balancing algorithm to make this decision based on factors like current server load, task complexity and server capabilities. Result forwarding logic, which the agent uses to receive the completed subtask results and then immediately forward them back to the orchestrator. It acts as a receiver and an asynchronous dispatcher for the final outputs. Let's quickly talk about the impacts applying this pattern can have. In addition to the extra infrastructure and systems that it requires, there's also a concern that the task submitted for parallelization are suitable for this type of processing. If a given task is too small or the subtasks are not truly independent, then the time spent managing the parallelization can outweigh its actual performance gains.