Comprehensive Analysis: Individual Paper Summaries
Following paper reviews are based on the papers contained in Further Reading. Refer to the Paper Visualization which was inspired by Paperscape. This summery aims to enhance the project Agents-eval and was generated with help provided by Claude Sonnet 4 🙏🏼🌟🙌🏼💕🤗
2025-08
[2508.03858] MI9 - Agent Intelligence Protocol: Runtime Governance for Agentic AI Systems
Evaluation Approach: Focuses on runtime governance and monitoring of agentic systems. Establishes protocols for continuous intelligence assessment and behavioral compliance monitoring during agent execution.
• Focus: Agentic systems - runtime governance and intelligence protocols • Relevance for Agents-eval: High - runtime monitoring protocols for continuous evaluation • Concrete Example: Implement MI9 protocol adapters to monitor agent decision-making patterns and compliance metrics in real-time
[2508.03682] Self-Questioning Language Models
Evaluation Approach: Develops self-assessment mechanisms where models generate and answer their own evaluation questions. Creates introspective evaluation loops for model uncertainty and capability assessment.
• Focus: LLM-based systems with self-evaluation capabilities • Relevance for Agents-eval: High - self-questioning mechanisms for automated evaluation • Concrete Example: Integrate self-questioning modules that generate domain-specific evaluation questions for agents to assess their own performance
[2508.00414] Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training
Evaluation Approach: Establishes evaluation metrics for research-oriented agents, focusing on deep reasoning capabilities, research methodology adherence, and scientific output quality assessment.
• Focus: Agentic systems - specifically research agents and foundation model training • Relevance for Agents-eval: Medium - research-specific evaluation methods • Concrete Example: Adapt cognitive kernel evaluation metrics to assess agent reasoning depth and research methodology compliance
2025-07
[2507.23276] How Far Are AI Scientists from Changing the World?
Evaluation Approach: Evaluates AI scientist capabilities through impact assessment, scientific contribution analysis, and research output quality metrics. Includes benchmarks for scientific discovery and innovation potential.
• Focus: Agentic systems - AI scientists and research agents • Relevance for Agents-eval: Medium - scientific impact evaluation methods • Concrete Example: Implement scientific contribution scoring system based on novelty, reproducibility, and potential impact metrics
[2507.22414] AutoCodeSherpa: Symbolic Explanations in AI Coding Agents
Evaluation Approach: Focuses on explainability evaluation for coding agents. Assesses quality of symbolic explanations, code reasoning transparency, and interpretability of agent decisions.
• Focus: Agentic systems - coding agents with explainability features • Relevance for Agents-eval: Medium - explainability assessment for coding agents • Concrete Example: Add explainability evaluation module that scores agent explanations using symbolic reasoning clarity metrics
[2507.21046] A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence
Evaluation Approach: Evaluates self-evolution capabilities in agents, including adaptation metrics, learning progression assessment, and capability expansion measurement over time.
• Focus: Agentic systems - self-evolving autonomous agents • Relevance for Agents-eval: High - longitudinal evaluation of agent evolution • Concrete Example: Implement evolution tracking dashboard that monitors agent capability changes and adaptation rates over time
[2507.18074] AlphaGo Moment for Model Architecture Discovery
Evaluation Approach: Evaluates automated architecture discovery agents, focusing on search efficiency, architecture quality, and optimization convergence metrics.
• Focus: Agentic systems - architecture discovery agents • Relevance for Agents-eval: Low - highly specialized for architecture discovery • Concrete Example: Adapt architecture quality metrics for evaluating any agent’s internal structure optimization
[2507.17311] EarthLink: A Self-Evolving AI Agent for Climate Science
Evaluation Approach: Domain-specific evaluation for climate science agents, including scientific accuracy, prediction quality, and environmental impact assessment capabilities.
• Focus: Agentic systems - domain-specific climate science agents • Relevance for Agents-eval: Low - highly domain-specific • Concrete Example: Extract domain-agnostic scientific accuracy evaluation methods for specialized knowledge agents
[2507.17257] Agent Identity Evals: Measuring Agentic Identity
Evaluation Approach: Develops identity consistency evaluation for agents, measuring personality persistence, behavioral coherence, and identity stability across interactions.
• Focus: Agentic systems - agent identity and personality evaluation • Relevance for Agents-eval: High - identity consistency evaluation framework • Concrete Example: Implement identity coherence scoring system that tracks agent personality consistency across different tasks
[2507.16940] AURA: A Multi-Modal Medical Agent for Understanding, Reasoning & Annotation
Evaluation Approach: Multi-modal evaluation for medical agents, including diagnostic accuracy, reasoning quality, and annotation precision across different medical data types.
• Focus: Agentic systems - multi-modal medical agents • Relevance for Agents-eval: Medium - multi-modal evaluation techniques • Concrete Example: Adapt multi-modal evaluation pipeline for agents handling diverse data types (text, images, structured data)
[2507.10584] ARPaCCino: An Agentic-RAG for Policy as Code Compliance
Evaluation Approach: Compliance evaluation for RAG-based agents, focusing on policy adherence, regulatory compliance accuracy, and code compliance verification.
• Focus: Agentic systems - RAG agents with compliance focus • Relevance for Agents-eval: Medium - compliance and policy adherence evaluation • Concrete Example: Build compliance evaluation module that checks agent outputs against predefined policy requirements
[2507.05178] CREW-WILDFIRE: Benchmarking Agentic Multi-Agent Collaborations at Scale
Evaluation Approach: Large-scale multi-agent evaluation using emergency response scenarios. Measures coordination, communication effectiveness, and collective decision-making in crisis situations.
• Focus: Agentic systems - multi-agent collaborative systems • Relevance for Agents-eval: Medium - multi-agent collaboration evaluation • Concrete Example: Implement team coordination metrics that evaluate agent communication patterns and task distribution efficiency
[2507.02825] Establishing Best Practices for Building Rigorous Agentic Benchmarks
Evaluation Approach: Meta-evaluation methodology providing guidelines for benchmark design, reproducibility standards, and evaluation framework validation.
• Focus: Agentic systems - evaluation methodology and best practices • Relevance for Agents-eval: Very High - foundational evaluation framework design • Concrete Example: Apply rigorous benchmark design principles including statistical validity, reproducibility checks, and bias detection protocols
[2507.02097] The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
Evaluation Approach: Evaluation framework for multi-agent recommender systems, including recommendation quality, user satisfaction, and system fairness assessment.
• Focus: Agentic systems - multi-agent recommender systems • Relevance for Agents-eval: Low - highly domain-specific for recommender systems • Concrete Example: Extract collaborative filtering evaluation metrics for any multi-agent system with recommendation components
2025-06
[2506.18096] Deep Research Agents: A Systematic Examination And Roadmap
Evaluation Approach: Comprehensive evaluation framework for research agents including literature review quality, hypothesis generation, experimental design, and scientific rigor assessment.
• Focus: Agentic systems - deep research agents • Relevance for Agents-eval: High - systematic evaluation methodology for complex agent tasks • Concrete Example: Implement research methodology evaluation pipeline that scores agent performance on systematic investigation tasks
[2506.16499] ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning
Evaluation Approach: Evaluates AI systems that optimize other AI systems, focusing on meta-learning capabilities, optimization effectiveness, and reasoning integration quality.
• Focus: Agentic systems - meta-AI optimization agents • Relevance for Agents-eval: Medium - meta-evaluation and optimization assessment • Concrete Example: Build meta-evaluation layer that assesses how well agents can evaluate and improve other agents
[2506.13131] AlphaEvolve: A coding agent for scientific and algorithmic discovery
Evaluation Approach: Scientific coding evaluation including algorithm novelty, implementation correctness, computational efficiency, and scientific contribution assessment.
• Focus: Agentic systems - scientific coding agents • Relevance for Agents-eval: Medium - scientific coding evaluation methods • Concrete Example: Implement algorithmic discovery scoring that evaluates code novelty, efficiency, and scientific validity
[2506.04133] TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems
Evaluation Approach: Security and trust evaluation for multi-agent systems, including risk assessment, trust measurement, and security vulnerability evaluation.
• Focus: Agentic systems - security and trust evaluation • Relevance for Agents-eval: High - safety and security evaluation framework • Concrete Example: Integrate TRiSM security evaluation modules that assess agent trustworthiness and risk levels
2025-05
[2505.22967] MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming
Evaluation Approach: Evaluates workflow generation agents with safety constraints, including workflow quality, safety compliance, and evolutionary optimization effectiveness.
• Focus: Agentic systems - workflow generation with safety constraints • Relevance for Agents-eval: High - safety-constrained evaluation methodology • Concrete Example: Implement safety-constrained workflow evaluation that checks agent outputs against safety requirements
[2505.22954] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Evaluation Approach: Long-term evolutionary evaluation of self-improving agents, including adaptation measurement, improvement trajectory analysis, and evolutionary fitness assessment.
• Focus: Agentic systems - self-improving evolutionary agents • Relevance for Agents-eval: High - long-term agent evolution tracking • Concrete Example: Build evolution monitoring system that tracks agent self-improvement over extended periods
[2505.22583] GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git
Evaluation Approach: Domain-specific evaluation for software development agents using Git operations, measuring code management, collaboration skills, and workflow understanding.
• Focus: Agentic systems - software development agents • Relevance for Agents-eval: Medium - domain-specific Git-based evaluation • Concrete Example: Adapt Git operation evaluation suite for any agent performing version control tasks
[2505.19764] Agentic Predictor: Performance Prediction for Agentic Workflows via Multi-View Encoding
Evaluation Approach: Predictive evaluation using multi-view encoding to forecast agent performance before full execution, enabling proactive optimization.
• Focus: Agentic systems - predictive performance evaluation • Relevance for Agents-eval: High - predictive evaluation for efficiency optimization • Concrete Example: Implement performance prediction module that estimates agent success rates before task execution
[2505.18946] SANNet: A Semantic-Aware Agentic AI Networking Framework for Multi-Agent Cross-Layer Coordination
Evaluation Approach: Network-aware evaluation for multi-agent systems, including coordination efficiency, communication overhead, and semantic understanding assessment.
• Focus: Agentic systems - networked multi-agent coordination • Relevance for Agents-eval: Medium - network-aware agent evaluation • Concrete Example: Add network coordination metrics that evaluate agent communication efficiency and semantic alignment
[2505.15872] InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
Evaluation Approach: Information seeking evaluation for RAG agents, including search quality, information relevance, and retrieval effectiveness assessment.
• Focus: Agentic systems - information seeking and RAG agents • Relevance for Agents-eval: Medium - information retrieval evaluation methods • Concrete Example: Implement information seeking benchmark that evaluates agent query formulation and retrieval quality
2025-04
[2504.19678] From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
Evaluation Approach: Comprehensive review of evaluation methods spanning from LLM reasoning assessment to full autonomous agent evaluation, bridging traditional and agentic evaluation.
• Focus: Both LLM and agentic systems - comprehensive evaluation survey • Relevance for Agents-eval: High - comprehensive evaluation methodology overview • Concrete Example: Use survey taxonomy to structure evaluation categories from basic reasoning to full autonomy
[2504.16902] Building A Secure Agentic AI Application Leveraging Google’s A2A Protocol
Evaluation Approach: Security evaluation for agentic applications using A2A protocol, focusing on authentication, authorization, and secure communication assessment.
• Focus: Agentic systems - secure application development • Relevance for Agents-eval: Medium - security evaluation using A2A protocol • Concrete Example: Implement A2A-based security evaluation that verifies agent authentication and secure communication protocols
2025-03
[2503.21460] Large Language Model Agent: A Survey on Methodology, Applications and Challenges
Evaluation Approach: Survey of LLM agent evaluation methods across different applications, including capability assessment, application-specific metrics, and challenge identification.
• Focus: LLM-based agents - comprehensive methodology survey • Relevance for Agents-eval: High - systematic agent evaluation methodology • Concrete Example: Structure evaluation framework using survey’s methodology taxonomy for different agent applications
[2503.16416] Survey on Evaluation of LLM-based Agents
Evaluation Approach: Comprehensive survey categorizing evaluation into capability assessment, behavioral analysis, and performance benchmarking with gap identification.
• Focus: LLM-based agents - systematic evaluation survey • Relevance for Agents-eval: Very High - systematic evaluation framework • Concrete Example: Implement three-tier evaluation system: capabilities, behaviors, and performance as suggested in survey
[2503.14713] TestForge: Feedback-Driven, Agentic Test Suite Generation
Evaluation Approach: Self-generating evaluation through automated test suite creation with feedback loops for continuous improvement and adaptation.
• Focus: Agentic systems - self-evaluating test generation • Relevance for Agents-eval: High - automated test generation and self-evaluation • Concrete Example: Build TestForge-inspired module that automatically generates evaluation tests based on agent performance feedback
[2503.13657] Why Do Multi-Agent LLM Systems Fail?
Evaluation Approach: Failure analysis evaluation focusing on identifying failure modes, root cause analysis, and system reliability assessment in multi-agent contexts.
• Focus: Multi-agent LLM systems - failure analysis evaluation • Relevance for Agents-eval: High - failure mode detection and analysis • Concrete Example: Implement failure analysis module that identifies common multi-agent failure patterns and root causes
[2503.08979] Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Direction
Evaluation Approach: Scientific discovery evaluation including research quality, discovery novelty, experimental design, and scientific impact assessment.
• Focus: Agentic systems - scientific discovery agents • Relevance for Agents-eval: Medium - scientific discovery evaluation methods • Concrete Example: Adapt scientific discovery metrics to evaluate any agent performing research or discovery tasks
[2503.06416] Advancing AI Negotiations: New Theory and Evidence from a Large-Scale Autonomous Negotiation Competition
Evaluation Approach: Negotiation performance evaluation including strategy effectiveness, outcome optimization, and competitive performance assessment.
• Focus: Agentic systems - autonomous negotiation agents • Relevance for Agents-eval: Low - highly specialized for negotiation tasks • Concrete Example: Extract strategic decision-making evaluation metrics for any agent performing competitive tasks
[2503.00237] Agentic AI Needs a Systems Theory
Evaluation Approach: Systems-theoretic evaluation approach focusing on emergent properties, system behavior analysis, and complexity assessment.
• Focus: Agentic systems - systems theory approach to evaluation • Relevance for Agents-eval: High - systems-level evaluation methodology • Concrete Example: Implement systems-theory evaluation that assesses agent emergent properties and complex system behaviors
2025-02
[2502.14776] SurveyX: Academic Survey Automation via Large Language Models
Evaluation Approach: Automated survey generation and analysis evaluation, including survey quality, response analysis accuracy, and research methodology compliance.
• Focus: LLM-based systems - automated survey generation • Relevance for Agents-eval: Low - highly specialized for survey automation • Concrete Example: Extract automated evaluation generation methods for creating evaluation surveys
[2502.05957] AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
Evaluation Approach: Zero-code agent evaluation focusing on automation quality, user experience, and framework effectiveness assessment.
• Focus: LLM-based agents - automated agent frameworks • Relevance for Agents-eval: Medium - automated evaluation framework design • Concrete Example: Implement zero-code evaluation interface that allows non-technical users to evaluate agents
[2502.02649] Fully Autonomous AI Agents Should Not be Developed
Evaluation Approach: Safety and ethics evaluation for autonomous agents, including risk assessment, ethical compliance, and safety constraint verification.
• Focus: Agentic systems - safety and ethics evaluation • Relevance for Agents-eval: High - safety and ethics evaluation framework • Concrete Example: Build safety evaluation module that assesses agent autonomy levels and associated risks
2025-01
[2501.16150] AI Agents for Computer Use: A Review of Instruction-based Computer Control, GUI Automation, and Operator Assistants
Evaluation Approach: Computer-use agent evaluation including accuracy metrics, user experience measures, safety assessments, and real-world usability testing.
• Focus: Agentic systems - computer-use and GUI automation agents • Relevance for Agents-eval: Medium - computer-use evaluation methods • Concrete Example: Implement GUI interaction evaluation suite that measures agent accuracy in computer control tasks
[2501.06590] ChemAgent
Evaluation Approach: Chemistry-specific agent evaluation including chemical knowledge accuracy, reaction prediction quality, and safety protocol compliance.
• Focus: Agentic systems - domain-specific chemistry agents • Relevance for Agents-eval: Low - highly domain-specific for chemistry • Concrete Example: Extract domain expertise evaluation methods for any specialized knowledge agent
[2501.06322] Multi-Agent Collaboration Mechanisms: A Survey of LLMs
Evaluation Approach: Collaboration mechanism evaluation including coordination efficiency, communication quality, and collective intelligence assessment.
• Focus: Multi-agent LLM systems - collaboration mechanisms • Relevance for Agents-eval: Medium - multi-agent collaboration evaluation • Concrete Example: Implement collaboration quality metrics that measure agent teamwork effectiveness
[2501.04227] Agent Laboratory: Using LLM Agents as Research Assistants
Evaluation Approach: Research assistant evaluation including research quality, methodology compliance, and scientific contribution assessment.
• Focus: LLM-based agents - research assistance • Relevance for Agents-eval: Medium - research assistance evaluation methods • Concrete Example: Build research assistant evaluation that scores agent contributions to scientific workflows
[2501.00881] Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents
Evaluation Approach: Industry-specific evaluation for vertical agents, including domain adaptation assessment, business impact measurement, and transformation effectiveness.
• Focus: Agentic systems - industry-specific vertical agents • Relevance for Agents-eval: Medium - industry adaptation evaluation methods • Concrete Example: Create industry adaptation evaluation framework that measures agent effectiveness across different domains
2024-12
[2412.17149] A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loop
Evaluation Approach: Iterative refinement evaluation with LLM-driven feedback loops, measuring optimization effectiveness, convergence quality, and system improvement.
• Focus: Agentic systems - multi-agent optimization systems • Relevance for Agents-eval: High - iterative evaluation with feedback loops • Concrete Example: Implement feedback-driven evaluation system that continuously refines evaluation criteria based on agent performance
[2412.04093] Practical Considerations for Agentic LLM Systems
Evaluation Approach: Practical deployment evaluation including system reliability, scalability assessment, maintenance requirements, and operational effectiveness.
• Focus: LLM-based agentic systems - practical deployment considerations • Relevance for Agents-eval: High - practical deployment evaluation considerations • Concrete Example: Add deployment readiness evaluation that assesses agent reliability and operational requirements
2024-11
[2411.13768] Evaluation-driven Approach to LLM Agents
Evaluation Approach: Evaluation-driven development methodology where assessment guides optimization, focusing on continuous improvement and performance-based refinement.
• Focus: LLM-based agents - evaluation-driven development • Relevance for Agents-eval: High - evaluation-driven development methodology • Concrete Example: Implement development pipeline that uses evaluation results to automatically suggest agent improvements
[2411.13543] BALROG: Benchmarking Agentic LLM and VLM Reasoning on Games
Evaluation Approach: Game-based reasoning evaluation using strategic environments to assess planning, decision-making, and competitive performance.
• Focus: Agentic systems - reasoning evaluation through games • Relevance for Agents-eval: Medium - game-based evaluation methods • Concrete Example: Create strategic reasoning benchmark using simplified game scenarios to evaluate agent decision-making
[2411.10478] Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
Evaluation Approach: ML workflow construction evaluation including pipeline quality, optimization effectiveness, and workflow validity assessment.
• Focus: LLM-based systems - ML workflow construction • Relevance for Agents-eval: Low - specialized for ML workflow construction • Concrete Example: Extract workflow construction evaluation metrics for agents that build complex processes
[2411.05285] A Taxonomy of AgentOps for Enabling Observability of Foundation Model Based Agents
Evaluation Approach: Operational observability evaluation through AgentOps taxonomy, focusing on runtime monitoring and system health assessment.
• Focus: Foundation model-based agents - operational monitoring • Relevance for Agents-eval: High - operational monitoring and observability • Concrete Example: Implement AgentOps monitoring dashboard that tracks agent operational metrics in real-time
2024-10
[2410.22457] Advancing Agentic Systems: Dynamic Task Decomposition, Tool Integration and Evaluation using Novel Metrics and Dataset
Evaluation Approach: Novel metrics for dynamic task decomposition and tool integration, including adaptability measurement and decomposition quality assessment.
• Focus: Agentic systems - task decomposition and tool integration • Relevance for Agents-eval: Very High - novel evaluation metrics and datasets • Concrete Example: Implement dynamic task decomposition evaluation that scores agent ability to break down complex tasks
[2410.14393] Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks
Evaluation Approach: Debugging agent evaluation including error detection accuracy, resolution effectiveness, and code improvement quality assessment.
• Focus: Agentic systems - debugging and error resolution agents • Relevance for Agents-eval: Medium - debugging effectiveness evaluation • Concrete Example: Build debugging evaluation suite that measures agent error detection and resolution capabilities
[2410.09713] Agentic Information Retrieval
Evaluation Approach: Information retrieval evaluation for autonomous agents, including search strategy assessment, relevance judgment, and retrieval effectiveness.
• Focus: Agentic systems - autonomous information retrieval • Relevance for Agents-eval: Medium - information retrieval evaluation methods • Concrete Example: Implement information retrieval evaluation that assesses agent search strategies and result quality
[2408.08435] Automated Design of Agentic Systems
Evaluation Approach: Automated system design evaluation including design quality, system effectiveness, and automation level assessment.
• Focus: Agentic systems - automated system design • Relevance for Agents-eval: Medium - automated design evaluation methods • Concrete Example: Create design quality evaluation that scores agent-generated system architectures
[2408.01768] Building Living Software Systems with Generative & Agentic AI
Evaluation Approach: Living systems evaluation including adaptability, evolution capability, and system lifespan assessment for generative and agentic systems.
• Focus: Agentic systems - living software systems • Relevance for Agents-eval: Medium - adaptive system evaluation methods • Concrete Example: Implement living system evaluation that tracks agent adaptation and evolution over time
2024-08
[2408.06361] Large Language Model Agent in Financial Trading: A Survey
Evaluation Approach: Financial trading evaluation including portfolio performance, risk management, market adaptation, and trading strategy effectiveness.
• Focus: LLM-based agents - financial trading applications • Relevance for Agents-eval: Low - highly domain-specific for financial trading • Concrete Example: Extract quantitative performance evaluation methods for any agent making sequential decisions
[2408.06292] The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Evaluation Approach: Scientific discovery evaluation including research novelty, experimental validity, publication quality, and scientific impact assessment.
• Focus: Agentic systems - automated scientific discovery • Relevance for Agents-eval: Medium - scientific discovery evaluation methods • Concrete Example: Implement scientific contribution evaluation that scores agent research outputs for novelty and validity
2024-04
[2404.13501] A Survey on the Memory Mechanism of Large Language Model based Agents
Evaluation Approach: Memory system evaluation including memory retention, retrieval accuracy, contextual relevance, and memory utilization effectiveness.
• Focus: LLM-based agents - memory mechanisms • Relevance for Agents-eval: High - memory system evaluation methods • Concrete Example: Build memory evaluation suite that tests agent memory retention, retrieval accuracy, and contextual usage
2024-02
[2402.06360] CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models
Evaluation Approach: Collaborative search evaluation including search coordination, result quality, collaboration effectiveness, and search strategy assessment.
• Focus: LLM-based agents - collaborative search • Relevance for Agents-eval: Low - specialized for collaborative search • Concrete Example: Extract collaborative task evaluation methods for any multi-agent coordination scenario
[2402.02716] Understanding the planning of LLM agents: A survey
Evaluation Approach: Planning capability evaluation including plan quality, execution effectiveness, adaptation ability, and strategic thinking assessment.
• Focus: LLM-based agents - planning capabilities • Relevance for Agents-eval: High - planning evaluation methods • Concrete Example: Implement planning evaluation suite that scores agent strategic thinking and plan execution quality
[2402.01030] Executable Code Actions Elicit Better LLM Agents
Evaluation Approach: Code execution evaluation including code quality, execution success, error handling, and practical implementation effectiveness.
• Focus: LLM-based agents - executable code generation • Relevance for Agents-eval: Medium - code execution evaluation methods • Concrete Example: Create code execution evaluation that measures agent coding accuracy and execution success rates
2023-08
[2308.11432] A Survey on Large Language Model based Autonomous Agents
Evaluation Approach: Comprehensive autonomous agent evaluation including capability assessment, autonomy measurement, and performance benchmarking across multiple dimensions.
• Focus: LLM-based autonomous agents - comprehensive evaluation survey • Relevance for Agents-eval: Very High - foundational comprehensive agent evaluation • Concrete Example: Use survey’s evaluation framework as foundation for multi-dimensional agent assessment structure
Conclusion
The comprehensive analysis of 50+ papers reveals a rapidly maturing field with clear consensus around key evaluation dimensions while highlighting significant opportunities for standardization and integration.