Multi-Agent Systems

41 summarised stories about Multi-Agent Systems, each linking back to the original source. Browse all topics →

Key players

Anthropic Google Claude Google DeepMind OpenAI Claude Code

Friday, 17 July 2026

Trinity: An Evolved LLM Coordinator

Sakana AI 33

Sakana AI developed TRINITY, a coordinator system that orchestrates multiple specialized AI models at test-time without modifying their weights, using an evolutionary algorithm to optimize a 20K-parameter routing mechanism. The system achieved an 86.2% pass@1 score on LiveCodeBench and generalized zero-shot to four unseen tasks while outperforming individual models including GPT-5 and Claude-4-Sonnet. This approach replaces the industry focus on scaling single monolithic models with collaborative multi-model systems that combine complementary strengths through dynamic task assignment.

Read the full story

Agent Orchestration Multi-Agent Systems

Learning to Orchestrate Agents in Natural Language with the Conductor

Sakana AI 17

Sakana AI trained a 7-billion-parameter Conductor model using reinforcement learning to manage and coordinate a team of other AI models by writing natural language instructions tailored to each task. The Conductor achieved 83.9% on LiveCodeBench and 87.5% on GPQA-Diamond, surpassing individual models in its pool while dynamically adapting its approach—using single queries for simple questions and constructing multi-step workflows for complex problems. This approach enables AI systems to leverage collective intelligence by learning to delegate tasks across diverse models rather than relying on fixed human-designed workflows.

Read the full story

Reinforcement Learning Agent Orchestration Sakana AI Multi-Agent Systems

Every AI story that matters,
in your inbox by 8am.

TLDRocket reads all relevant sources, removes duplicate coverage, and summarises the day in two minutes. Follow companies and topics for alerts, or get the briefing in Slack. Free, no spam, unsubscribe anytime.