LLM Agent Orchestration

LLM: Large Language Model

LLM agent orchestration studies how to organize language models, tools, and specialized agents into systems that can solve tasks requiring multiple steps. Common approaches include sequential and hierarchical pipelines, graph-based workflows, parallel delegation, adaptive routing, and plan–execute–reflect loops, with memory and verification used to manage state and catch errors. Work is evaluated on tasks such as coding, web research, tool use, and domain workflows, using measures that include success, cost, latency, and reliability. A central open tension is that added coordination can improve difficult tasks but also increase cost and failure opportunities, and its benefits depend on the model, task structure, and quality of execution controls.

Latest papers

463 papers61 papers in the last four weeks, up 74% on the four weeks before. 0.4% of all new papers.

Self-Programmed Execution for Language-Model Agents
May 7, 2026Luke J. O'ConnorLLM Agent Orchestration LLM Agents
Harvard Medical School Boston, MA 02115
NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research
May 7, 2026Lujia Zhong, Yihao Xia, Jianwei Zhang +4Multi-Agent LLM Systems LLM Agent Orchestration
1Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California · 2Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California · 3Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California
Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs
May 7, 2026Elizabeth Mieczkowski, Alexander Ku, Tiwalayo Eisape +5Multi-Agent Coordination LLM Agent Orchestration
Princeton University · University of Cambridge · MIT +1
More Is Not Always Better: Cross-Component Interference in LLM Agent Scaffolding
May 7, 2026Ming LiuLLM Agent Orchestration LLM Agent Evaluation
Amazon
Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use
May 6, 2026Francisco Javier Arceo, Varsha Prasad NarsingLLM Agent Orchestration AI Agent Security
Red Hat AI · Boston, USA
Design Conductor 2.0: An agent builds a TurboQuant inference accelerator in 80 hours
May 6, 2026The Verkor Team, Ravi Krishna, Suresh Krishna +1AI Accelerator Inference Multi-Agent LLM Systems
The Verkor Team
Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation
May 6, 2026Zhiqing Cui, Haotong Xie, Jiahao Yuan +11Multi-Agent System Optimization Multi-Agent Orchestration
Project lead.
SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting
May 6, 2026Kuan-Hao Tseng, Niruth Bogahawatta, Yasod Ginige +3LLM Agent Orchestration LLM Agents
University of Sydney, Sydney, Australia · University of New South Wales, Sydney, Australia
When Context Hurts: The Crossover Effect of Knowledge Transfer on Multi-Agent Design Exploration
May 5, 2026Saranyan VigrahamSoftware Engineering Agents LLM Agent Orchestration
Meta
Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing
May 5, 2026Danny Hoang, Ryan Matthiessen, Christopher Miller +5Manufacturing Multi-Agent LLM Systems
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT, USA · Connecticut Center for Advanced Technology, East Hartford, CT, USA · Quinnipiac University, Hamden, CT, USA +1
From Intent to Execution: Composing Agentic Workflows with Agent Recommendation
May 5, 2026Kishan Athrey, Ramin Pishehvar, Brian Riordan +1LLM Agent Orchestration Multi-Agent Systems
Cisco Systems Inc.
ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration
May 4, 2026Ruofeng Yang, Yongcan Li, Shuai LiAI Agent Reliability Multi-Agent LLM Systems
Shanghai Jiao Tong University · Shanghai Innovation Institute
Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces
May 4, 2026Chenchen ZhangMulti-Agent LLM Systems LLM Agent Orchestration
Independent Researcher
Beyond State Machines: Executing Network Procedures with Agentic Tool-Calling Sequences
May 4, 2026Purna Sai Garigipati, Onur Ayan, Kishor Chandra Joshi +1LLM Agent Orchestration Tool-Using Agents
Heisenberg Research Center, Huawei Technologies Duesseldorf GmbH, 80992 Munich, Germany · Eindhoven University of Technology, Eindhoven, The Netherlands
EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
May 4, 2026Xiyuan Zhou, Ruixi Zou, Xinlei Wang +4Multi-Agent LLM Systems Software Engineering Agents
Nanyang Technological University · The Chinese University of Hong Kong, Shenzhen · INSAIT, Sofia University “St. Kliment Ohridski” +2
AAFLOW: Scalable Patterns for Agentic AI Workflows
May 4, 2026Arup Kumar Sarker, Mills Staylor, Aymen Alsaadi +3LLM Agent Orchestration Agentic Workflows
Department of Computer Science, Biocomplexity Institute and Initiative University of Virginia Charlottesville, VA, USA · Department of Computer Science, Rutgers University New Brunswick, NJ, USA · Biocomplexity Institute and Initiative University of Virginia Charlottesville, VA, USA +1
CyberAId: AI-Driven Cybersecurity for Financial Service Providers
May 3, 2026George Fatouros, Georgios Makridis, John Soldatos +18Multi-Agent LLM Systems Autonomous Cyber Defense
Innov-Acts, Cyprus · University of Piraeus, Greece · Unparallel Innovation, Portugal +7
6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
May 2, 2026Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah6G Networks LLM Agent Orchestration
Department of Computer and Network Engineering, United Arab Emirates University, UAE · Research Institute for Digital Future, Khalifa University, UAE
Agentic AI Systems Should Be Designed as Marginal Token Allocators
May 2, 2026Siqi ZhuLLM Agent Orchestration LLM Agent Evaluation
University of Illinois Urbana-Champaign
Position: agentic AI orchestration should be Bayes-consistent
May 1, 2026Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27LLM Agent Orchestration AI Control
PolyShape, Greece. · NTUA, Greece. · ESSEC Business School, Singapore. +19
Affordance Agent Harness: Verification-Gated Skill Orchestration
May 1, 2026Haojian Huang, Jiahao Shi, Yinchuan Li +1Multimodal Grounding LLM Agent Orchestration
HKUST(GZ) · Knowin AI · Harbin Engineering University +1
SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters
May 1, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuLLM Inference Efficiency LLM Agent Orchestration
Agent Capsules: Quality-Gated Granularity Control for Multi-Agent LLM Pipelines
May 1, 2026Aninda RayLLM Inference Efficiency Multi-Agent LLM Systems
In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks
Apr 30, 2026Simon Dennis, Michael Diamond, Rivaan Patil +2LLM Prompting LLM Agent Orchestration
i14, University of Melbourne · i14
TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data
Apr 30, 2026Rong LuLLM Agent Orchestration Tool-Using Agents
Heterogeneous Scientific Foundation Model Collaboration
Apr 30, 2026Zihao Li, Jiaru Zou, Feihao Fang +6AI for Science LLM Agent Orchestration
University of Illinois Urbana-Champaign
Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations
Apr 29, 2026Bochao Liu, Zhipeng Qian, Yang Zhao +10Continual Learning for LLM Agents LLM Agent Orchestration
Kuaishou Technology
Autonomous LLM Agents & CTFs: A Second Look
Apr 29, 2026Youness Bouchari, Matteo Boffa, Marco Mellia +3LLM Agent Orchestration AI Agent Security Benchmarks
Politecnico di Torino · Universit`a di Torino · Huawei Technologies France
SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms
Apr 29, 2026Yu Shen, Shiyang Liu, Qihang He +14Customer Support Automation LLM Agent Orchestration
TEG, Tencent Inc., China · School of Computer Science, Peking University, China
ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents
Apr 28, 2026Zhou Hanlin, Chan Huah YongLLM Agent Memory LLM Agent Orchestration
School of Computer Sciences, Universiti Sains Malaysia (USM), Penang, Malaysia · Xiamen Software Vocational & Technical College, Xiamen, China

May 7, 2026J/K move · Enter open · S save

Self-Programmed Execution for Language-Model Agents

Luke J. O'Connor

Harvard Medical School Boston, MA 02115

LLM Agent Orchestration LLM Agents

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At the heart of a language model agent is an orchestrator program which is responsible for the state transition between consecutive turns. In existing systems, this program is implemented in the harness. This paper introduces self-programmed execution (SPE), in which the model completion is itself the orchestrator program, and the harness evaluates this program but does not impose any fixed orchestration policy. Formally, an SPE state of an agentic machine is one from which a model completion can load any successor state up to an embedding, and an instance of an SPE state is one which executes the model completion as a program. In practice, the program requires a language with unusual features, as its source code doubles as the content of the model's context window. I therefore introduce Spell, a Lisp dialect in which programs can edit and re-evaluate themselves, and effectful expressions like model invocations are structured such that re-evaluating an edited program does not replay its side effects. Experiments with existing models, not trained for SPE or Spell, show that frontier models can learn Spell in-context and accomplish challenging agentic tasks. These results demonstrate how a LM can act as an agent without any fixed orchestration policy, and they raise the question of what self-orchestration strategies might be learned by a model trained for self-programmed execution. Code is available at https://github.com/lukejoconnor/spell.