--- license: cc-by-4.0 language: - en - zh tags: - survey - llm-agents - agent-harness - agent-frameworks - harness-engineering - evaluation - agent-memory - multi-agent-systems pretty_name: "Agent Harness for Large Language Model Agents: A Survey" size_categories: - n<1K ---
[English](README.md) | [中文](README_zh.md) # Agent Harness for Large Language Model Agents: A Survey [![GitHub Stars](https://img.shields.io/github/stars/Gloriaameng/LLM-Agent-Harness-Survey?style=social)](https://github.com/Gloriaameng/LLM-Agent-Harness-Survey/stargazers) [![License](https://img.shields.io/badge/License-CC--BY--4.0-blue.svg)](LICENSE) [![Papers](https://img.shields.io/badge/Papers-110%2B-green)]() [![Version](https://img.shields.io/badge/Version-v2-orange)]() [![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97-Dataset-yellow)](https://huggingface.co/datasets/GloriaaaM/LLM-Agent-Harness-Survey)

H=(E,T,C,S,L,V) Six-Component Architecture

> ⭐ **This survey is actively maintained. If you find it useful, please star the repo to stay updated and help others find it.** --- > **The agent execution harness — not the model — is the primary determinant of agent reliability at scale.** > This survey formalizes the harness as a first-class architectural object **H = (E, T, C, S, L, V)**, surveys 110+ papers, blogs and reports across 23 systems, and maps 9 open technical challenges. > 📄 **[Read the Paper](./Agent_Harness_for_LLM_Agents__A_Survey_v3.pdf)** (Latest version, under review) > 🌐 **[Preprints Version (v2)](https://www.preprints.org/manuscript/202604.0428/v2)** > ✉️ Corrections & suggestions: gloriamenng@gmail.com (Qianyu Meng); wangyanan@mail.dlut.edu.cn (Yanan Wang); chenliyi@xiaohongshu.com (Liyi Chen) If you find this survey useful, please cite: ```bibtex @misc{meng2026agentharness, title = {Agent Harness for Large Language Model Agents: A Survey}, author = {Meng, Qianyu* and Wang, Yanan* and Chen, Liyi and Wang, Qimeng and Lu, Chengqiang and Wu, Wei and Gao, Yan and Wu, Yi and Hu, Yao}, year = {2026}, doi = {10.20944/preprints202604.0428.v2}, url = {https://www.preprints.org/manuscript/202604.0428/v2}, publisher = {Preprints}, note = {* Equal contribution. Under review, latest version available at: https://github.com/Gloriaameng/LLM-Agent-Harness-Survey} } ``` --- ## 🆕 News & Updates - **[2026-04-03]** Initial release - **[2026-04-07]** Repo updated - **[2026-04-09]** Preprint released --- ## Table of Contents - [Overview](#overview) - [Historical Timeline](#historical-timeline) - [Harness Completeness Matrix](#harness-completeness-matrix) - [Paper List](#paper-list) - [Historical Lineages](#historical-lineages) - [Harness Taxonomy](#harness-taxonomy) - [Technical Challenges](#technical-challenges) - [Security & Sandboxing](#security--sandboxing) - [Evaluation & Benchmarking](#evaluation--benchmarking) - [Protocol Standardization](#protocol-standardization) - [Runtime Context Management](#runtime-context-management) - [Tool Use & Registry](#tool-use--registry) - [Memory Architecture](#memory-architecture) - [Planning & Reasoning](#planning--reasoning) - [Multi-Agent Coordination](#multi-agent-coordination) - [Compute Economics](#compute-economics) - [Emerging Topics](#emerging-topics) - [Future Directions](#future-directions) - [Citation](#citation) - [Update Log](#update-log) --- ## Overview LLM agents are increasingly deployed in agentic settings where they autonomously plan, use tools, and act in multi-step environments. The dominant narrative attributes agent performance to the underlying model. **This survey challenges that assumption.** We introduce a formal definition of the **agent execution harness** as a six-component tuple: | Component | Symbol | Role | |-----------|--------|------| | Execution Loop | **E** | Observe-think-act cycle, termination conditions, error recovery | | Tool Registry | **T** | Typed tool catalog, routing, monitoring, schema validation | | Context Manager | **C** | What enters the context window, compaction, retrieval | | State Store | **S** | Persistence across turns/sessions, crash recovery | | Lifecycle Hooks | **L** | Auth, logging, policy enforcement, instrumentation | | Evaluation Interface | **V** | Action trajectories, intermediate states, success signals | **Key empirical evidence that harnesses matter:** - 🔥 Pi Research: Grok Code Fast 1 jumped from **6.7% → 68.3%** on SWE-bench by changing *only* the harness edit-tool format — model unchanged - 💀 OpenAI Codex: **1M lines of code, 0 hand-written** over 5 months — failure attributed not to model capability but to "underspecified environments" - ⚡ Stripe Minions: **1,300 PRs/week, 0 human-written code** — harness-first engineering - 📉 METR: benchmark-passing PRs have a **24.2pp lower** human merge rate, gap widening at 9.6pp/year — evaluation harness validity crisis - 💬 *"The harness is the chassis; the model is the engine."* — practitioner consensus, 2026

Root Cause Analysis

### What This Survey Accomplishes **Conceptual contribution:** We formalize the agent harness as an architectural object with six governable components (E, T, C, S, L, V), elevating it from implicit infrastructure to an explicit research target. **Empirical scope:** We systematically review 110+ papers spanning academic research (evaluation benchmarks, security frameworks, memory architectures) and production deployments (Stripe, OpenAI, Cursor, METR), establishing that harness design is a binding constraint on deployed agent reliability. **Methodological advance:** We introduce the **Harness Completeness Matrix** — a structured assessment framework mapping which of the six components each system implements — enabling direct comparison across heterogeneous agent systems that prior surveys could not evaluate on common terms. **Open challenges identified:** We document nine technical challenges where current research provides partial solutions but no production-grade infrastructure: formal security models, cross-harness portability, protocol interoperability (MCP/A2A), context economics at 1M+ tokens/task, Byzantine fault tolerance in multi-agent systems, and compositional verification. **Practitioner-academic bridge:** Unlike prior surveys focused exclusively on model capabilities or isolated components (memory, planning, tool use), we synthesize peer-reviewed research with production deployment reports to show where theory meets practice — and where critical gaps remain. **Intended audience:** Researchers designing agent infrastructure, practitioners building production systems, and evaluators seeking to understand why benchmark performance often fails to predict deployment outcomes. --- ## Historical Timeline

Historical Evolution of Agent Harnesses

| Year | Milestone | Significance | |------|-----------|--------------| | 1997–2005 | JUnit, TestNG, xUnit family | Software test harness paradigm; standardized observe-assert lifecycle | | 2016 | OpenAI Gym (Brockman et al.) | RL environment harness; step/reset API becomes canonical interface | | 2022 Nov | ChatGPT public release; LangChain emerges | LLM-native agent frameworks begin; tool-use as first-class citizen | | 2023 | ReAct, Toolformer, MemGPT, Reflexion, Voyager, AutoGPT | Core agent patterns: reasoning-acting, memory, reflection, skill accumulation | | 2023 | CAMEL, ChatDev, Generative Agents | Multi-agent coordination; social simulation harnesses | | 2023 | AgentBench, SWE-bench | Agent evaluation infrastructure emerges | | 2024 | MetaGPT, WebArena, ToolLLM, SWE-agent, OSWorld | Full-stack harnesses; real-world environment benchmarks | | 2024 | CodeAct, LATS, Tree of Thoughts | Structured action spaces; search-augmented planning | | 2024 Nov | Anthropic releases MCP protocol | First major tool↔harness standardization (2–15ms latency) | | 2025 | HAL, AIOS, LangGraph | Benchmark unification (21,730 rollouts); OS-level scheduling (2.1× speedup) | | 2025 | Google releases A2A protocol | Agent↔agent standardization (50–200ms) | | 2025 | MemoryOS, SkillsBench†, AgentBound† | Memory OS abstraction; skills-as-context (+16.2pp); safety certification | | 2026 Jan–Mar | AgencyBench†, SandboxEscapeBench†, PRISM†, AEGIS†, SkillFortify†, Schema First† | Compute economics; 15–35% escape rates; runtime security; schema discipline | *† preprint* --- ## Harness Completeness Matrix **Legend:** ✓ full support · ≈ partial · ✗ absent
Category System E T C S L V
Full-Stack
Harnesses		 Claude Code ✓✓✓✓✓≈
OpenClaw / PRISM ✓✓✓✓✓✓
AIOS ✓✓✓✓✓≈
OpenHands ✓✓✓✓✓≈
Multi-Agent
Harnesses		 MetaGPT ✓✓≈≈≈≈
AutoGen ✓✓≈≈≈≈
ChatDev ✓≈≈≈≈≈
CAMEL ✓≈≈≈✗≈
DeerFlow ✓✓≈≈≈≈
DeepAgents ✓✓≈≈≈≈
General
Frameworks		 LangChain ✓✓✓≈≈✗
LangGraph ✓≈≈≈✗✗
LlamaIndex ≈✓✓≈✗✗
Specialized
Harnesses		 SWE-agent ✓✓✓≈≈✓
Capability
Modules		 MemGPT ✗✗✓✓✗✗
Voyager ✓✓≈✓✗≈
Reflexion ≈✗≈✓✗≈
Generative Agents ✓✗≈✓✗≈
Concordia ✓✗≈✓✗≈
Evaluation
Infrastructure		 HAL ✓✓≈≈≈✓
AgentBench ✓≈≈≈✗✓
OSWorld ✓≈≈≈✗✓
BrowserGym ✓✓≈≈✗✓
--- ## Paper List ### Historical Lineages #### Software Test Harnesses (1990s–2000s) - JUnit: **"JUnit: A Cook's Tour"**. *Beck & Gamma.* Java Report, 4(5), May 1999. [[Article](http://junit.sourceforge.net/doc/cookstour/cookstour.htm)] #### RL Environment Harnesses (2016–2022) - OpenAI Gym: **"OpenAI Gym"**. *Brockman et al.* arXiv 2016. [[Paper](https://arxiv.org/abs/1606.01540)] [[Code](https://github.com/openai/gym)] - Gymnasium: **"Gymnasium: A Standard Interface for Reinforcement Learning Environments"**. *Towers et al.* NeurIPS 2025. [[Paper](https://arxiv.org/abs/2407.17032)] [[Code](https://github.com/Farama-Foundation/Gymnasium)] #### Early LLM Agent Frameworks (2023–2024) - ReAct: **"ReAct: Synergizing Reasoning and Acting in Language Models"**. *Yao et al.* ICLR 2023. [[Paper](https://arxiv.org/abs/2210.03629)] [[Code](https://github.com/ysymyth/ReAct)] - Toolformer: **"Toolformer: Language Models Can Teach Themselves to Use Tools"**. *Schick et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2302.04761)] - AutoGPT: **"Auto-GPT: An Autonomous GPT-4 Experiment"**. *Gravitas et al.* GitHub 2023. [[Code](https://github.com/Significant-Gravitas/AutoGPT)] - LangChain: **"LangChain: Building Applications with LLMs through Composability"**. *Chase et al.* GitHub 2022. [[Code](https://github.com/langchain-ai/langchain)] --- ### Harness Taxonomy **What we classify:** We categorize agent systems by **harness completeness** — which of the six components (E, T, C, S, L, V) each system implements — distinguishing full-stack harnesses (all six components) from specialized frameworks (partial implementations). **Why it matters:** Prior taxonomies classified agents by application domain (coding, web navigation, embodied AI) or model architecture (single-agent, multi-agent). These categorizations cannot explain why systems with similar models achieve different reliability outcomes. Our harness-centric taxonomy reveals that production-grade systems converge on full ETCSLV implementations, while research prototypes often implement only 2-3 components. **Key finding:** No agent framework can achieve production reliability without implementing **all six governance components**. Systems missing L-component (lifecycle hooks) cannot enforce safety policies. Systems missing V-component (evaluation interfaces) cannot debug failures. Systems missing S-component (state persistence) cannot recover from crashes. #### Full-Stack Harnesses - PRISM/OpenClaw: **"OpenClaw PRISM: A Zero-Fork, Defense-in-Depth Runtime Security Layer for Tool-Augmented LLM Agents"**. *Li.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.11853)] - AIOS: **"AIOS: LLM Agent Operating System"**. *Mei et al.* COLM 2025. [[Paper](https://arxiv.org/abs/2403.16971)] [[Code](https://github.com/agiresearch/AIOS)] - OpenHands: **"OpenHands: An Open Platform for AI Software Developers as Generalist Agents"**. *Wang et al.* ICLR 2025. [[Paper](https://arxiv.org/abs/2407.16741)] [[Code](https://github.com/All-Hands-AI/OpenHands)] - SWE-agent: **"SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering"**. *Yang et al.* NeurIPS 2024. [[Paper](https://arxiv.org/abs/2405.15793)] [[Code](https://github.com/SWE-agent/SWE-agent)] - HAL: **"Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation"**. *Kapoor et al.* ICLR 2026. [[Paper](https://arxiv.org/abs/2510.11977)] #### Multi-Agent Harnesses - MetaGPT: **"MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework"**. *Hong et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2308.00352)] [[Code](https://github.com/geekan/MetaGPT)] - AutoGen: **"AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation"**. *Wu et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2308.08155)] [[Code](https://github.com/microsoft/autogen)] - ChatDev: **"ChatDev: Communicative Agents for Software Development"**. *Qian et al.* ACL 2024. [[Paper](https://arxiv.org/abs/2307.07924)] [[Code](https://github.com/OpenBMB/ChatDev)] - CAMEL: **"CAMEL: Communicative Agents for 'Mind' Exploration of Large Language Model Society"**. *Li et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2303.17760)] [[Code](https://github.com/camel-ai/camel)] #### Frameworks & Modules - LangGraph: **"LangGraph: Build Resilient Language Agents as Graphs"**. *LangChain team.* GitHub 2024. [[Code](https://github.com/langchain-ai/langgraph)] - MemGPT: **"MemGPT: Towards LLMs as Operating Systems"**. *Packer et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2310.08560)] [[Code](https://github.com/cpacker/MemGPT)] - Voyager: **"Voyager: An Open-Ended Embodied Agent with Large Language Models"**. *Wang et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2305.16291)] [[Code](https://github.com/MineDojo/Voyager)] - Reflexion: **"Reflexion: Language Agents with Verbal Reinforcement Learning"**. *Shinn et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2303.11366)] [[Code](https://github.com/noahshinn/reflexion)] - Generative Agents: **"Generative Agents: Interactive Simulacra of Human Behavior"**. *Park et al.* UIST 2023. [[Paper](https://arxiv.org/abs/2304.03442)] [[Code](https://github.com/joonspk-research/generative_agents)] - LangChain: **"LangChain: Building Applications with LLMs through Composability"**. *Chase et al.* GitHub 2022. [[Code](https://github.com/langchain-ai/langchain)] - LlamaIndex: **"LlamaIndex: A Data Framework for LLM Applications"**. *Liu et al.* GitHub 2022. [[Code](https://github.com/run-llama/llama_index)] - DeerFlow: **"DeerFlow: Distributed Workflow Engine for LLM Agents"**. *GitHub 2024.* [[Code](https://github.com/modelscope/DeerFlow)] - DeepAgents: **"DeepAgents: Multi-Agent Framework for Deep Learning"**. *GitHub 2024.* [[Code](https://github.com/deepagents/deepagents)] #### Evaluation Infrastructure - AgentBench: **"AgentBench: Evaluating LLMs as Agents"**. *Liu et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2308.03688)] [[Code](https://github.com/THUDM/AgentBench)] - SWE-bench: **"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?"**. *Jimenez et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2310.06770)] [[Code](https://github.com/swebench/SWE-bench)] - OSWorld: **"OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments"**. *Xie et al.* NeurIPS 2024. [[Paper](https://arxiv.org/abs/2404.07972)] [[Code](https://github.com/xlang-ai/OSWorld)] - WebArena: **"WebArena: A Realistic Web Environment for Building Autonomous Agents"**. *Zhou et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2307.13854)] [[Code](https://github.com/web-arena-x/webarena)] - GAIA: **"GAIA: A Benchmark for General AI Assistants"**. *Mialon et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2311.12983)] - Mind2Web: **"Mind2Web: Towards a Generalist Agent for the Web"**. *Deng et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2306.06070)] - AgentBoard: **"AgentBoard: An Analytical Evaluation Board of Multi-Turn LLM Agents"**. *Ma et al.* NeurIPS 2024. [[Paper](https://arxiv.org/abs/2401.13178)] - InterCode: **"InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback"**. *Yang et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2306.14898)] --- ### Technical Challenges **Scope:** We identify nine open research problems where current harness infrastructure provides partial solutions but no production-grade, generalizable frameworks. Each challenge represents a gap between what academic prototypes demonstrate and what deployed systems require. **Evaluation criteria:** For each challenge, we assess: (1) **Academic coverage** — peer-reviewed solutions, if any; (2) **Production maturity** — whether any deployed system has solved it at scale; (3) **Blocking severity** — whether the unsolved problem prevents deployment or merely limits performance. **Cross-cutting theme:** Most challenges stem from the **composition problem** — individual components (security sandboxes, memory stores, evaluation metrics) work in isolation, but combining them into a coherent harness surfaces emergent failures. Formal verification, portability testing, and protocol interoperability all require compositional reasoning that current research has not addressed. #### Security & Sandboxing **Challenge:** Agent sandboxes must prevent malicious tool use while permitting legitimate system access — a harder problem than traditional application sandboxing because agents **intentionally** interact with sensitive resources (file systems, network APIs, shell commands). **State of the art:** SandboxEscapeBench shows frontier models can escape containers; PRISM demonstrates zero-fork defense-in-depth is possible but requires OS-level instrumentation. No generalizable sandbox exists that works across all agent use cases without manual per-task configuration. **Open problems:** Compositional security properties (proving agent + harness + environment satisfy a safety specification) remain unsolved. Current defenses are reactive (detecting escapes) rather than preventive (proving impossibility of escape). > **Key numbers:** SandboxEscapeBench — frontier LLMs achieve **15–35% container escape rates**; PRISM — 10-hook zero-fork runtime reduces escape to near-zero with <5ms overhead. - SandboxEscapeBench†: **"Quantifying Frontier LLM Capabilities for Container Sandbox Escape"**. *Marchand et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.02277)] - InjecAgent: **"InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents"**. *Zhan et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2403.02691)] - ToolHijacker†: **"Prompt Injection Attack to Tool Selection in LLM Agents"**. *Shi et al.* NDSS 2026. [[Paper](https://arxiv.org/abs/2504.19793)] - Securing MCP†: **"Securing the Model Context Protocol (MCP): Risks, Controls, and Governance"**. *Errico et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2511.20920)] - SHIELDA†: **"SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows"**. *Zhou et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2508.07935)] - PALADIN†: **"PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases"**. *Vuddanti et al.* ICLR 2026. [[Paper](https://arxiv.org/abs/2509.25238)] - AgentBound†: **"Securing AI Agent Execution"**. *Bühler et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2510.21236)] - AgentSys†: **"AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management"**. *Wen et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.07398)] - Indirect Prompt Injection: **"Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection"**. *Greshake et al.* AISec 2023. [[Paper](https://arxiv.org/abs/2302.12173)] - AgentHarm†: **"AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents"**. *Andriushchenko et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2410.09024)] - TrustAgent: **"TrustAgent: Towards Safe and Trustworthy LLM-Based Agents"**. *Hua et al.* EMNLP 2024. [[Paper](https://arxiv.org/abs/2402.01586)] - ToolEmu†: **"Identifying the Risks of LM Agents with an LM-Emulated Sandbox"**. *Ruan et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2309.15817)] - Ignore Previous Prompt: **"Ignore Previous Prompt: Attack Techniques For Language Models"**. *Perez & Ribeiro.* NeurIPS ML Safety Workshop 2022. [[Paper](https://arxiv.org/abs/2211.09527)] #### Evaluation & Benchmarking > **Key numbers:** HAL unified **21,730 rollouts**, compressing weeks of evaluation to hours; OSWorld reports **28% false negative rate** in automated evaluation; METR finds benchmark-passing PRs have **24.2pp lower** human merge rate, widening at 9.6pp/year. - AgencyBench†: **"AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts"**. *Li et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2601.11044)] - AEGIS†: **"AEGIS: No Tool Call Left Unchecked -- A Pre-Execution Firewall and Audit Layer for AI Agents"**. *Yuan et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.12621)] - Hell or High Water†: **"Hell or High Water: Evaluating Agentic Recovery from External Failures"**. *Wang et al.* COLM 2025. [[Paper](https://arxiv.org/abs/2508.11027)] - SearchLLM†: **"Aligning Large Language Models with Searcher Preferences"**. *Wu et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.10473)] - Meta-Harness†: **"Meta-Harness: End-to-End Optimization of Model Harnesses"**. *Lee et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.28052)] - TheAgentCompany†: **"TheAgentCompany: Benchmarking LLM Agents on Consequential Real-World Tasks"**. *Xu et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2412.14161)] - BrowserGym†: **"The BrowserGym Ecosystem for Web Agent Research"**. *Le Sellier De Chezelles et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2412.05467)] - WorkArena†: **"WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?"**. *Drouin et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2403.07718)] - R-Judge: **"R-Judge: Benchmarking Safety Risk Awareness for LLM Agents"**. *Yuan et al.* EMNLP 2024. [[Paper](https://arxiv.org/abs/2401.10019)] - R2E: **"R2E: Turning any GitHub Repository into a Programming Agent Environment"**. *Jain et al.* ICML 2024. [[Paper](https://proceedings.mlr.press/v235/jain24c.html)] - Evaluation Survey: **"Evaluation and Benchmarking of LLM Agents: A Survey"**. *Mohammadi et al.* KDD 2025. [[Paper](https://arxiv.org/abs/2507.21504)] - PentestJudge†: **"PentestJudge: Judging Agent Behavior Against Operational Requirements"**. *Caldwell et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2508.02921)] #### Protocol Standardization > **Key numbers:** MCP (tool↔harness): 2–15ms latency; A2A (agent↔agent): 50–200ms; ACP (intent-level, IBM) — three protocols serve complementary roles. - MCP: **"Model Context Protocol"**. *Anthropic.* Technical Report 2024. [[Spec](https://modelcontextprotocol.io)] - A2A: **"Agent-to-Agent Protocol"**. *Google.* Technical Report 2025. [[Spec](https://google.github.io/A2A/)] - Protocol Comparison†: **"A Survey of Agent Interoperability Protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)"**. *Ehtesham et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2505.02279)] - Gorilla: **"Gorilla: Large Language Model Connected with Massive APIs"**. *Patil et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2305.15334)] [[Code](https://github.com/ShishirPatil/gorilla)] #### Runtime Context Management > **Key numbers:** SkillsBench — curated skill injection yields **+16.2pp** improvement; "Lost in the Middle" effect documented; long-context models shift the problem from *retention* to *salience*. - SkillsBench†: **"SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks"**. *Li et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.12670)] - ReadAgent: **"A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts"**. *Lee et al.* ICML 2024. [[Paper](https://arxiv.org/abs/2402.09727)] - MemoryOS: **"Memory OS of AI Agent"**. *Kang et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2506.06326)] - CoALA: **"Cognitive Architectures for Language Agents"**. *Sumers et al.* TMLR 2024. [[Paper](https://arxiv.org/abs/2309.02427)] - SkillFortify†: **"Formal Analysis and Supply Chain Security for Agentic AI Skills"**. *Bhardwaj.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.00195)] - Lost in the Middle: **"Lost in the Middle: How Language Models Use Long Contexts"**. *Liu et al.* TACL 2024. [[Paper](https://arxiv.org/abs/2307.03172)] - Context Engineering Survey†: **"Context Engineering: A Survey of 1,400 Papers on Effective Context Management for LLM Agents"**. *Mei et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2507.13334)] #### Tool Use & Registry > **Key numbers:** Vercel found removing **80% of tools** helped more than any model upgrade; Schema First (Sigdel & Baral, 2026) — a controlled pilot showing that schema-based tool contracts reduce *interface* misuse but not *semantic* misuse, with end-task success at zero across all conditions, suggesting interface design alone is insufficient for tool reliability; CodeAct outperforms on **17/17 Mint benchmarks** with **−20% turns**. - CodeAct: **"Executable Code Actions Elicit Better LLM Agents"**. *Wang et al.* ICML 2024. [[Paper](https://arxiv.org/abs/2402.01030)] [[Code](https://github.com/xingyaoww/code-act)] - Schema First†: **"Schema First Tool APIs for LLM Agents: A Controlled Study of Tool Misuse, Recovery, and Budgeted Performance"**. *Sigdel & Baral.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.13404)] - ToolLLM: **"ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs"**. *Qin et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2307.16789)] [[Code](https://github.com/OpenBMB/ToolBench)] - ToolSandbox†: **"ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities"**. *Lu et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2408.04682)] - AutoTool†: **"AutoTool: Efficient Tool Selection for Large Language Model Agents"**. *Jia & Li.* AAAI 2026. [[Paper](https://arxiv.org/abs/2511.14650)] - Tool Learning Survey: **"Tool Learning with Large Language Models: A Survey"**. *Qu et al.* Frontiers of Computer Science 2024. [[Paper](https://arxiv.org/abs/2405.17935)] - GoEX†: **"GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications"**. *Patil et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2404.06921)] - AgentTuning: **"AgentTuning: Enabling Generalized Agent Abilities for LLMs"**. *Zeng et al.* ACL 2024. [[Paper](https://arxiv.org/abs/2310.12823)] #### Memory Architecture > **Key numbers:** Mem0 achieves **90% token reduction** vs full-context; Zep temporal knowledge: **+18.5% QA accuracy**; Agent Workflow Memory: **+14.9%** on Mind2Web. Six architectural patterns: flat buffer → hierarchical → episodic → semantic → procedural → graph. - Agent Workflow Memory (AWM)†: **"Agent Workflow Memory"**. *Wang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2409.07429)] - Mem0†: **"Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory"**. *Khant et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2504.19413)] - A-MEM†: **"A-MEM: Agentic Memory for LLM Agents"**. *Xu et al.* NeurIPS 2025. [[Paper](https://arxiv.org/abs/2502.12110)] - MemAct†: **"Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks"**. *Zhang et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2510.12635)] - Memory Survey†: **"Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers"**. *Du.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.07670)] - MemoryBank: **"MemoryBank: Enhancing Large Language Models with Long-Term Memory"**. *Zhong et al.* AAAI 2024. [[Paper](https://arxiv.org/abs/2305.10250)] - LoCoMo†: **"Evaluating Very Long-Term Conversational Memory of LLM Agents"**. *Maharana et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2402.17753)] - Memory Mechanisms Survey†: **"A Survey on the Memory Mechanism of Large Language Model Based Agents"**. *Zhang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2404.13501)] - Evo-Memory†: **"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory"**. *Wei et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2511.20857)] #### Planning & Reasoning > **Key numbers:** SWE-agent ACI study shows interface design outweighs model capability as the primary performance determinant. LATS integrates MCTS with language model feedback for state-space search. Plan-on-Graph enables adaptive self-correcting planning on knowledge graphs through guidance, memory, and reflection mechanisms. - Tree of Thoughts: **"Tree of Thoughts: Deliberate Problem Solving with Large Language Models"**. *Yao et al.* NeurIPS 2023. [[Paper](https://arxiv.org/abs/2305.10601)] [[Code](https://github.com/princeton-nlp/tree-of-thought-llm)] - LATS: **"Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models"**. *Zhou et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2310.04406)] [[Code](https://github.com/lapisrocks/LanguageAgentTreeSearch)] - Plan-on-Graph: **"Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs"**. *Chen et al.* NeurIPS 2024. [[Paper](https://proceedings.neurips.cc/paper_files/paper/2024/hash/4254e856d01a5e7b7ea050477c3ef9b9-Abstract-Conference.html)] - AFlow†: **"AFlow: Automating Agentic Workflow Generation"**. *Zhang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2410.10762)] - Agent Q†: **"Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents"**. *Putta et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2408.07199)] - OPENDEV†: **"Building Effective AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned"**. *Bui.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.05344)] - AOrchestra†: **"AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration"**. *Ruan et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.03786)] - RAP: **"Reasoning with Language Model is Planning with World Model"**. *Hao et al.* EMNLP 2023. [[Paper](https://arxiv.org/abs/2305.14992)] - Inner Monologue: **"Inner Monologue: Embodied Reasoning Through Planning with Language Models"**. *Huang et al.* CoRL 2022. [[Paper](https://arxiv.org/abs/2207.05608)] - Agent-Oriented Planning: **"Agent-Oriented Planning in Multi-Agent Systems"**. *Li et al.* ICLR 2025. [[Paper](https://arxiv.org/abs/2410.02189)] - ExACT†: **"ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning"**. *Yu et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2410.02052)] #### Multi-Agent Coordination > **Key numbers:** AgencyBench — agents achieve **48.4% success on native SDK harness** vs substantially lower on independent harnesses, demonstrating tight harness-agent coupling. Byzantine fault tolerance remains an open problem for adversarial multi-agent settings.

Multi-Agent Coordination Topologies

- SAGA†: **"SAGA: A Security Architecture for Governing AI Agentic Systems"**. *Syros et al.* NDSS 2026. [[Paper](https://arxiv.org/abs/2504.21034)] - MAS-FIRE†: **"MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems"**. *Jia et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2602.19843)] - Byzantine fault tolerance†: **"Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance"**. *Zheng et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2511.10400)] - Multi-agent baseline study†: **"Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline"**. *Xu et al.* arXiv 2026. [[Paper](https://arxiv.org/abs/2601.12307)] - AgentVerse†: **"AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors"**. *Chen et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2308.10848)] - Mixture-of-Agents†: **"Mixture-of-Agents Enhances Large Language Model Capabilities"**. *Wang et al.* arXiv 2024. [[Paper](https://arxiv.org/abs/2406.04692)] - Multi-Agent Survey: **"Large Language Model Based Multi-Agents: A Survey of Progress and Challenges"**. *Guo et al.* IJCAI 2024. [[Paper](https://arxiv.org/abs/2402.01680)] - Concordia†: **"Generative Agent-Based Modeling with Actions Grounded in Physical, Social, or Digital Space Using Concordia"**. *Vezhnevets et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2312.03664)] #### Compute Economics > **Key numbers:** OpenRouter reports **13T tokens/week** (Feb 2026), doubling every 4 weeks; AgencyBench measures **1M tokens/task** average; 1000× agent compute growth projected by 2027; AIOS achieves **2.1× throughput speedup** via proper agent scheduling. - Repo2Run†: **"Repo2Run: Automated Building Executable Environment for Code Repository at Scale"**. *Hu et al.* arXiv 2025. [[Paper](https://arxiv.org/abs/2502.13681)] - Policy-First†: **"Guardrails as Infrastructure: Policy-First Control for Tool-Orchestrated Workflows"**. *Sigdel & Baral.* arXiv 2026. [[Paper](https://arxiv.org/abs/2603.18059)] --- ### Emerging Topics - Self-Evolving Agents Survey†: **"A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence"**. *Gao et al.* TMLR 2026. [[Paper](https://arxiv.org/abs/2507.21046)] - Self-RAG: **"Self-RAG: Learning to Retrieve, Generate, and Critique Through Self-Reflection"**. *Asai et al.* ICLR 2024. [[Paper](https://arxiv.org/abs/2310.11511)] - Constitutional AI: **"Constitutional AI: Harmlessness from AI Feedback"**. *Bai et al.* arXiv 2022. [[Paper](https://arxiv.org/abs/2212.08073)] - AppAgent†: **"AppAgent: Multimodal Agents as Smartphone Users"**. *Zhang et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2312.13771)] #### Related Surveys - LLM Agents Survey: **"A Survey on Large Language Model Based Autonomous Agents"**. *Wang et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2308.11432)] - Rise of LLM Agents: **"The Rise and Potential of Large Language Model Based Agents: A Survey"**. *Xi et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2309.07864)] - LLM Survey: **"A Survey of Large Language Models"**. *Zhao et al.* arXiv 2023. [[Paper](https://arxiv.org/abs/2303.18223)] - AI Agent Systems†: **"AI Agent Systems: Architectures, Applications, and Evaluation"**. *Xu.* arXiv 2025. [[Paper](https://arxiv.org/abs/2601.01743)] #### Practitioner Reports & Industry Insights > Production deployment experiences from Stripe, OpenAI, Cursor, METR, and other frontier practitioners. - Stripe Minions: **"Minions: Stripe's one-shot, end-to-end coding agents"**. *Gray.* Stripe Dev Blog, Feb 2026. [[Blog](https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents)] - Harness Engineering (OpenAI): **"Harness engineering: leveraging Codex in an agent-first world"**. *Lopopolo.* OpenAI Blog, Feb 2026. [[Blog](https://openai.com/index/harness-engineering/)] - Self-Driving Codebases: **"Towards self-driving codebases"**. *Lin.* Cursor Blog, Feb 2026. [[Blog](https://cursor.com/blog/self-driving-codebases)] - METR SWE-bench Analysis: **"Many SWE-bench-Passing PRs Would Not Be Merged into Main"**. *Whitfill et al.* METR Notes, Mar 2026. [[Report](https://metr.org/notes/2026-03-10-many-swe-bench-passing-prs-would-not-be-merged-into-main/)] --- ### Future Directions Eight open research directions identified in the survey (no curated paper list — these are forward-looking): - **Formal Harness Specification Language** — DSL for specifying and verifying H=(E,T,C,S,L,V) components - **Cross-Harness Benchmark Suite** — portability testing across incompatible harness ecosystems - **Security Taxonomy & Threat Model** — extension of OWASP Top 10 to agent harness attack surfaces - **Protocol Interoperability (MCP/A2A)** — bridging tool-level and agent-level protocols - **Long-Horizon Evaluation Methodology** — metrics that don't degrade under multi-session, multi-day tasks - **Harness-Aware Fine-Tuning** — training models that are aware of their execution environment - **Memory Interface Standardization** — portable memory APIs across flat, episodic, and graph stores - **Harness Transparency Specification** — auditability and explainability for runtime decisions --- ## Citation See BibTeX at the top of this README. --- ## Update Log | Version | Date | Changes | |---------|------|---------| | v1 | 2026-04-03 | Initial release | | v2 | 2026-04-07 | Repo updated | | v3 | 2026-04-09 | Preprint added | ---

† denotes preprint, not yet peer-reviewed.
This survey is under active development; the full manuscript will be released soon.
Maintained by Qianyu Meng & Liyi Chen. PRs welcome for missing papers or updated links.