The solo agent proved the task could be done in 3,918 tokens. The team used 45,069 — meaning 91% of the team's compute was coordination overhead. Requirements writing, architecture reviews, bug reports, status updates, and re-reading context that each agent couldn't see.
The Tech Lead consumed 63% of all team tokens — acting as the information bottleneck, relaying messages, integrating code, and managing context. The solo agent used fewer tokens than any single team member except the PM.
chooseOperation too restrictive on empty inputs, invalid CSS selector in triggerVisualFeedback, early return in compute skipping history write. Fixes assigned.The Tech Lead consumed 63% of all team tokens because every piece of information had to pass through them. Requirements from PM, code from SWEs, bugs from QA — all routed through one agent. The hierarchy created a single point of context management that was more expensive than the actual coding.
QA filed 5 bugs after the integration. But the solo agent — with full context in one call — didn't make those bugs in the first place. When one agent holds the entire design in memory, there are no handoff errors, no integration mismatches, no "I thought you were handling that."
24% of team output was alignment overhead: writing requirements docs, status updates, bug reports, architecture specs. The solo agent spent zero tokens on alignment because there was nobody to align with. That's the ROI question: does the marginal quality improvement justify 12× the cost?
A calculator fits in one context window. For tasks requiring genuine specialization — a distributed system, a multi-page app with API integrations — the team approach may win. The question is: how many of your team's tasks actually need that complexity, vs. how many are calculator-sized?