Scaling Laws for AI Agents: What Google DeepMind Discovered скачать в хорошем качестве

Scaling Laws for AI Agents: What Google DeepMind Discovered

*More Agents = Better Performance? The Research Says Otherwise* 🔗 AI Engineering Consultancy: https://brainqub3.com 🔗 AI Fact-Checking Tool: https://check.brainqub3.com --- Breaking down "Towards a Science of Scaling Agent Systems" from Google Research and DeepMind — a paper that challenges the widespread assumption that multi-agent architectures automatically outperform single agents. The key insight: we can actually predict when multi-agent systems will improve over a single agent baseline and when they'll degrade as you scale. This matters because while you don't own the foundation models, you do own the orchestration — and these choices have measurable effects. In this video I cover: The five coordination architectures tested (single agent, independent, decentralized, centralized, hybrid) Runtime behavioral metrics that predict scaling behaviour: coordination overhead, message density, redundancy rate, coordination efficiency, and error amplification Why Finance Agent benefits from multi-agent while Plan Craft falls apart The three interaction effects that explain most failure modes The "baseline paradox" — why adding agents to an already-strong single agent system can be the fastest way to make it worse The practical takeaway: treat multi-agent as a tool that only wins when task structure supports parallelism and decomposability. If your single agent already performs well, more agents may just accelerate degradation. Paper: https://arxiv.org/pdf/2512.08296 --- #AIAgents #MultiAgentSystems #AIEngineering #LLMs #AIResearch #AgenticAI