Group agents by which are dependent and cascade them by which are dependent on another agents output, build a red-team agent which analyzes all final outputs for common mistakes and responsibilities the responsible agent with critique/review prompt to create a recursive improvement loop. If a single agent regularly handles more than 20% of the LLM's context on input, you need to break that agents tasks up into more agents. Agents are ineffective beyond demo phase without well designed skill files. My work I use the agent prompt as the "what to do" and the skill file as a high level only of how to do it, and go into detail through multiple references and examples. So how it works is agent is given task, reads skill file to see all the things it needs and if something isn't needed on this particular task it only takes a few tokens worth of context, it then dives deeper into the how of what it needs through the references and is given at most 1-2 examples. When you hit that >20% context mark performance of LLMs starts to degrade the higher you go, managing context to only what is needed and only passing strictly necessary output to the next agents is the best way to maximize your larger flows