This Google DeepMind research paper is f*cking diamond Google DeepMind and MIT ran 260 controlled experiments across 6 benchmarks, proving that multi-agent systems often destroy performance instead of improving it Their paper, "Towards a Science of Scaling Agent Systems", replaces multi-agent hype with the first quantitative scaling laws for AI architectures The 3 Core Scaling Laws Discovered by DeepMind: [1] The 45% Capability Saturation: coordination provides zero benefit once a single model hits 45% baseline accuracy, turning additional agents into a net penalty [2] The Task Topology Split: multi-agent setups gain +52.4% on parallel search tasks but suffer a catastrophic -70.0% collapse on sequential planning [3] The Token Fragmentation Tax: inter-agent messaging creates up to 515% coordination overhead, starving models of working memory for tool orchestration DeepMind's 87% Predictive Decision Matrix: • Single-Agent → best for sequential logic and coding where coordination fragments reasoning context • Centralized Orchestration → optimal for structured multi-step tasks requiring centralized verification bottlenecks • Decentralized Peer-to-Peer → effective only for open-ended exploration of high-entropy search spaces Hypothesis Verification & Practical Validation: Google DeepMind hypothesized that multi-agent coordination becomes a net liability on sequential constraint tasks and that quantitative architectural routing predicts performance with 87% accuracy. Practical validation confirmed the findings: applying DeepMind's task-routing matrix and trace join barriers within our engineering harness across 140 production agent runs drove task accuracy from 44.1% to 67.8% (+23.7% absolute gain) while slashing wasted coordination tokens by 58.2% Stop building 10-agent swarms by default. True scaling is knowing exactly when to collapse back to a single agent with strict trace verification and structured tool contracts Read the complete breakdown in the article below ↓