we stopped letting agents plan 3 steps ahead, reliability got better fast

Curious if anyone else has seen this. We were giving agents too much room to "think ahead" and it honestly made them worse. Not always in obvious ways either, they looked smart in traces, but reliability kept dipping once the task touched real tools, CRM Automation, or any kind of **Workflow Automation** with messy state. What helped was making the agent plan exactly one next action, do it, re-read the new state, then decide again. Way less elegant on paper. But **AI Agents** started failing less, especially around **Lead qualification** flows, tool routing, and handoffs between steps. My guess is long plans get brittle fast because the world changes underneath them. A tool returns something weird, a field is blank, a user replies off-script, a Voice AI call goes slightly sideways, and now steps 2 and 3 are based on assumptions that arent true anymore. We still use **Multi-agent Systems** in some places, but even there, smaller bounded decisions seem more stable than one agent making a grand master-plan and and trying to stick to it. Not saying planning is bad. Just that in actual AI Automation work, especially when agents touch external systems, less foresight weirdly seems to work better. Has anyone benchmarked this more formally, or seen the opposite? idk if this is just an eval artifact on our side or a real pattern.