I stopped trying to build one super-agent and split it into 4 narrow agents. Reliability went way up.

For a while I kept making the same mistake a lot of people make with agent builds: I was trying to make one smart agent do everything. One prompt. One context window. One place for reasoning. One place for tools. One place for memory. One place for execution. In demos, it looked great. In real use, it kept doing the stuff I’m sure most of you have seen too: it would re-do work it already did, lose track of what step it was on, call the wrong tool, over-answer simple tasks, and occasionally make a weird jump because too many responsibilities were living in the same brain. So I rebuilt the workflow in a much more boring way. Instead of one general-purpose agent, I split it into 4 narrower agents with very specific jobs: The first agent only handles intake. Its job is to understand the request, clean it up, extract the actual task, and turn messy input into a structured handoff. The second agent only handles research. It gathers the information it needs, checks the relevant sources, and passes back a tighter packet of context instead of a giant pile of raw data. The third agent only handles action. No big-picture reasoning, no open-ended wandering. Just take the structured task plus context and do the thing it’s supposed to do. The fourth agent is basically review + escalation. It checks whether the output is actually usable, whether confidence is high enough, and whether the task should be kicked to a human instead of pretending everything is fine. That change helped way more than I expected. Not because the system got smarter, but because it got simpler. Each agent had fewer tools. Each prompt got shorter. Each failure became easier to spot. Each handoff became easier to inspect. And when something broke, I could actually tell where it broke. that was the biggest shift for me. When I had one super-agent, every failure felt fuzzy. You’d get a bad result, but it was hard to tell if the problem was prompt design, tool selection, missing context, memory confusion, or the model just taking a weird route. Once I split the workflow up, the failure points got obvious fast. If intake was weak, the task was framed wrong. If research was weak, context was incomplete. If action was weak, the execution logic needed work. If review caught something, it usually meant the workflow needed a human checkpoint earlier than I thought. It also changed how I think about agentic systems in general. I’m a lot less interested now in making one agent feel magical, and a lot more interested in making the whole system predictable. Honestly, most of the value seems to come from role clarity, constrained execution, and clean handoffs, not from raw autonomy. The more serious the workflow, the less I want a genius agent. I want a boring system that does the right thing most of the time and knows when to stop. Curious if other people here have hit the same wall. Are you still building around one main agent, or have you moved toward multi-agent setups with narrower roles?