Originally published on tamiz.pro.
The field of AI agents has moved rapidly from single-model executors to complex multi-agent orchestration. But after running 157 agent deployments across diverse task domains, one pattern emerged with striking consistency: planning quality predicts success far better than execution speed or model size. This isn't just theoretical—it's a practical lesson that's reshaping how engineers architect agent fleets, giving rise to what we're now calling Orca-style agents: hierarchical, planning-first systems that separate the expensive business of thinking from the cheaper business of doing.
The Experiment: 157 Agent Runs
Over six months, our team deployed and monitored 157 distinct agent runs across four primary use cases: code generation pipelines, automated testing workflows, infrastructure-as-code provisioning, and data transformation tasks. Each run varied along three dimensions:
Architecture: Single-agent vs. flat multi-agent vs. hierarchical (Orca-style)
