LLM Agent Workflow Optimization

LLM: Large Language Model

This field studies how to organize language models, tools, memory, and decision steps so agents can complete multi-step tasks effectively and efficiently. Approaches include fixed workflows and DAGs, query-level model routing, reinforcement learning and evolutionary search, and trace-guided prompt or harness refinement; they are tested in settings such as coding, web use, retrieval, and scientific research. Systems work also optimizes execution through caching, scheduling, parallelism, and compiling repeated behavior into deterministic workflows. A central tension is whether adaptive workflows improve on reusable fixed designs enough to justify their added cost and complexity, while maintaining reliability and generalizing beyond the tasks used for optimization.

Latest papers

262 papers59 papers in the last four weeks, up 69% on the four weeks before. 0.3% of all new papers.

MEGA: Self-Evolving Agent Optimization Infrastructure via Wisdom Graph
Aug 11, 2026Jung Hwan Lee, Kyu Ho Lee, Gwang Hoon YooMulti-Agent System Optimization Agent Harness Optimization
Mind AI Tech Report
FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows
Aug 10, 2026Shuo Hao, You Lu, Bihuan Chen +1Agentic Workflows LLM Agent Workflow Optimization
College of Computer Science and Artificial Intelligence, Fudan University, China
The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows
Aug 7, 2026Junbo Li, Boyi Liu, Canwen Xu +5Large Language Model-Guided Optimization LLM Agent Workflow Optimization
The University of Texas at Austin · Snowflake
Online Monitoring and Corrective Steering of Programming Agents
Aug 7, 2026Shuyang Liu, Saman Dehghan, Ji Young Kim +3Software Engineering Agents LLM Agent Workflow Optimization
University of Illinois Urbana-Champaign, USA · IBM, USA
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Aug 6, 2026Varun Ursekar, Apaar Shanker, Yash Maurya +4Large Language Model-Guided Optimization LLM Agent Evaluation
Scale AI
CodeGrep: An RL-Trained Retrieval Agent for LLM Coding Agents
Aug 6, 2026Wuya Chen, Yihao yang, Yang Cao +1AI Coding Agents LLM Agent Workflow Optimization
1Netease Guangzhou AI Lab · 2Independent Researcher
Global Optimization and Inference-Time Region Grafting for Agentic Workflows
Aug 3, 2026Donghyeok Koh, Gyuwan Kim, Jinyeong Bak +4Inference-Time Optimization Agentic Workflows
1HBNU · 2UCSB · 3SKKU +1
TraceCompiler: Skill-Guided Mining and Compilation of LLM Agent Traces into Mostly Deterministic Workflows
Aug 3, 2026Salma El Yadouni, Guanyi LiAgentic Workflows LLM-Based Program Synthesis
EPFL · EPFL, Lausanne, Switzerland · Binome Technologies +1
AiFlow: Token-Native Reactive Orchestration with Bounded Backpressure for Streaming LLM Applications
Aug 1, 2026Qunhui ZhangLLM Serving LLM Agent Orchestration
School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark
Jul 31, 2026Natan Vidra, Alina Kapanova, Arun Kanhai +1Agentic Workflows LLM Agent Workflow Optimization
1Anote AI · 2Cornell University · 3CUNY +1
Agent-UCT: Upper Confidence Bounds Applied to Trees for Cost-Aware Agentic Workflow Optimization
Jul 27, 2026Yang Li, Hai Liu, Dian Shao +9Retrieval-Augmented Generation Cost-Aware Inference
The University of Hong Kong · School of Artificial Intelligence, Jiangxi Science and Technology Normal University · The Hong Kong University of Science and Technology +2
Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems
Jul 26, 2026Mingzhou Fan, Siyuan Xu, Mingxuan YuanMulti-Agent System Optimization LLM Agent Orchestration
Agent Team Work Zone: An Automated, Persistent Workspace for Long-Lived Claude Code Agent Teams
Jul 24, 2026Shouren WangLLM Agent Memory LLM Agent Workflow Optimization
Case Western Reserve University
Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured Agent Skills
Jul 23, 2026Zibin Lin, Shengli Zhang, Taotao Wang +3LLM Agent Workflow Optimization LLM Agent Skill Learning
Shenzhen University, Shenzhen, China
Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
Jul 17, 2026Zhengyu Chen, Teng Xiao, Huaisheng Zhu +3Agent Harness Optimization LLM Agent Workflow Optimization
Meituan · Allen Institute for AI · Independent
Structured Feedback Improves Repair in an LLM Agent Loop
Jul 15, 2026Jaideep Ray, Ankit GoyalLLM Agent Workflow Optimization LLM Agent Evaluation
Independent Researcher
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Jul 14, 2026Junjie Yin, Xinyu FengSoftware Engineering Agents LLM Agent Evaluation
University of Tennessee, Knoxville, TN 37996, USA · Microsoft, Redmond, WA 98052, USA
TTHE: Test-Time Harness Evolution
Jul 9, 2026Jun Nie, Yonggang Zhang, Jun Song +5Test-Time Adaptation Agent Harness Optimization
Hong Kong Baptist University · University of Science and Technology of China · Hong Kong Generative AI Research & Development Center, The Hong Kong University of Science and Technology +1
Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
Jul 8, 2026Arun MalikCost-Aware Inference Agentic Workflows
Microsoft Azure Networking
A Workflow-Aware Serving Layer for Agentic Applications
Jul 3, 2026Jiayi Qian, Zishen Wan, Hanchen Yang +3Cost-Aware Inference Agentic Workflows
Columbia University New York, NY, USA · Intel Los Angeles, CA, USA
Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows
Jul 1, 2026Karthikeya Aditya Vissa, Sankalp Mane, Ananya Mantravadi +2Tool-Augmented Language Model Agents LLM Agent Workflow Optimization
Centific
SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks
Jun 30, 2026Seongho Son, Sangwoong Yoon, Jiahua Tang +3Software Engineering Agents LLM Agent Workflow Optimization
University College London, United Kingdom · Ulsan National Institute of Science and Technology, South Korea · PSL Research University, France +1
Contrastive Reflection for Iterative Prompt Optimization
Jun 29, 2026Derek Koh, Jinghui Mo, Benjamin H. Le +7LLM Self-Refinement LLM Agent Workflow Optimization
LinkedIn Sunnyvale, California, USA
Beyond Static Endpoints: Tool Programs as an Interface for Flexible Agentic Web Services
Jun 18, 2026Mugeng Liu, Shuoqi Li, Yixuan Zhang +1LLM Agent Orchestration Agentic Workflows
School of Computer Science, Peking University, Beijing, China · School of Software & Microelectronics, Peking University, Beijing, China · Institute for Artificial Intelligence, Peking University, Beijing, China
FAPO: Fully Automated Prompt Optimization of Multi-Step LLM Pipelines
Jun 17, 2026Paul Kassianik, Baturay Saglam, Huaibo Zhao +4Large Language Model-Guided Optimization LLM Agent Workflow Optimization
Foundation AI–Cisco Systems Inc. · Yale University
Recursive Self-Evolving Agents via Held-Out Selection
Jun 17, 2026Michael Nguyen, Quoc Nguyen, Paul VuongLLM Agent Evaluation LLM Agent Workflow Optimization
School of Information Technology Monash University Malaysia
DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling
Jun 14, 2026Kanishk Kushwaha, Vikrant Vinod Bansode, Harsh Vardhan +1LLM Inference Efficiency Multi-Agent Orchestration
Gati Shakti Vishwavidyalaya · IBM Research
Remember, Don't Re-read: Stateful ReAct Agents for Token-Efficient Autonomous Experimentation
Jun 12, 2026Faramarz JabbarvaziriLarge Language Model-Guided Optimization LLM Agent Workflow Optimization
Towards Direct Latent-Space Synthesis for Parallel Branches in LLM-Agent Workflows
Jun 12, 2026Shikun Liu, Mufei Li, Dongqi Fu +5Multi-Agent LLM Systems KV-Cache Management
1Georgia Institute of Technology · 2Meta
HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
Jun 12, 2026Tingyang Chen, Shuo Lu, Kang Zhao +11LLM Agent Orchestration Agent Harness Optimization
Darwin Agent Team

Aug 11, 2026J/K move · Enter open · S save

MEGA: Self-Evolving Agent Optimization Infrastructure via Wisdom Graph

Jung Hwan Lee, Kyu Ho Lee, Gwang Hoon Yoo

Mind AI Tech Report

Multi-Agent System Optimization Agent Harness Optimization LLM Agent Workflow Optimization Graph-Based Agent Memory Self-Evolving Agents

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As coding agents increasingly handle implementation, the central challenge shifts from building individual agents to building an infrastructure that systematically improves them. Current approaches optimize agent systems without accumulating transferable knowledge, accumulate knowledge without compositional reasoning over it, and lack a mechanism for that knowledge to self-evolve through operational evidence. MEGA (Meta Evaluation-Grounded Adaptation) addresses these gaps as a self-evolving infrastructure: each optimization cycle produces durable assets, compositional reasoning over those assets guides subsequent optimization, and operational evidence refines both the accumulated wisdom and the reasoning that governs it. Layer 1 distills reusable wisdom from agent sessions through behavioral-pattern clustering and empirical A/B validation, transforming each process into a durable asset. Layer 2 decomposes these assets into atomic PCR (Primary-Context-Resultant) units within a typed Wisdom Graph and performs deductive, abductive, and inductive reasoning to expand implicit relations; it then assembles context-specific execution plans through compositional retrieval that surfaces bridging knowledge unreachable by embedding similarity alone. Layer 3 performs multi-agent collaborative optimization over heterogeneous agent workflows (code nodes, LLM calls, and tool-using agents), attributing improvement effects to specific strategy changes through controlled evaluation that eliminates data variance. Evidence fed back from Layer 3 drives the self-evolution of both the curation strategies that govern wisdom composition and the optimization trajectories accumulated across runs. The result is an infrastructure in which optimizing an agent system and evolving the knowledge that guides optimization are one and the same process.