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Agent Orchestration

23 summarised stories about Agent Orchestration, each linking back to the original source. Browse all topics →

Friday, 17 July 2026

Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph

MarkTechPost 3 days ago

A tutorial demonstrates building an agentic event-venue operator using MongoDB Atlas for persistent memory, Voyage for multimodal embeddings, and LangGraph for agent orchestration, with a fictional tennis tournament scenario where an agent retrieves visitor history and operational context to make real-time decisions during weather disruptions. The demo includes a four-tab UI, FastAPI backend, vector and hybrid search endpoints, vision RAG for document retrieval, and optional Langfuse tracing, with all operational records, semantic memory, embeddings, and agent actions stored in a single MongoDB Atlas layer rather than scattered across separate systems. The implementation is presented as a reference demo for builders rather than a production platform, with deployment options for local development and Vercel hosting.

Read at MarkTechPost
Agent Orchestration

Trinity: An Evolved LLM Coordinator

Sakana AI

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 at Sakana AI
Multi-Agent Systems Agent Orchestration

Learning to Orchestrate Agents in Natural Language with the Conductor

Sakana AI

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 at Sakana AI
Reinforcement Learning Sakana AI Multi-Agent Systems Agent Orchestration

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