When people talk about AI agents, they often focus on execution — calling tools, running steps, completing tasks. But once you start building real systems, you realize execution isn’t the hard part. Control is. Knowing what should act, when it should act, and how to deal with the fact that LLMs are inherently unpredictable is where things get messy.
𝗦𝗶𝗻𝗴𝗹𝗲-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 sound simple, but they don’t age well. As you keep adding workflows, rules, and edge cases into one giant prompt, things slowly fall apart. Instructions in the middle get ignored, the agent starts drifting from the original intent, and tool calls become unreliable. These systems only work when the problem is small and tightly scoped — which is why they feel great in places like code editors, but struggle badly once real, multi-step workflows show up.
𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 try to fix this by splitting work across agents, but they introduce a new kind of pain. Coordinating agents is hard, and one small deviation can ripple through the whole workflow. You can add orchestrators and guardrails, but the more control you add, the more robotic the system becomes. Ironically, the system gets less “agentic” as you try to make it safer.
What tends to work best in practice is a middle path: 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗮𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝘄𝗲𝗹𝗹-𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝘀𝗸𝗶𝗹𝗹𝘀. One agent handles the conversation and reasoning, while specific skills take care of individual workflows using their own prompts and tools. This keeps things predictable without killing flexibility. There’s no massive prompt, fewer tool mistakes, and the system still feels natural to use.
𝗧𝗵𝗲 𝗹𝗲𝘀𝘀𝗼𝗻 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: the future isn’t about piling on more agents. It’s about drawing better boundaries and delegating carefully. Too much autonomy turns into chaos. Too much control turns into rigidity. Real-world systems succeed somewhere in between.
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