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Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems
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AI Agents are changing the way work gets done, both in consumer and enterprise domains. However, the design patterns and architectures to build highly capable agents or multi-agent systems are still developing, and the understanding of the implication of various design choices and algorithms is still evolving. In this paper, we present our work on building a novel web agent, Agent-E \footnote{Our code is available at \url{https://github.com/EmergenceAI/Agent-E}}. Agent-E introduces numerous architectural improvements over prior state-of-the-art web agents such as hierarchical architecture, flexible DOM distillation and denoising method, and the concept of \textit{change observation} to guide the agent towards more accurate performance. We first present the results of an evaluation of Agent-E on WebVoyager benchmark dataset and show that Agent-E beats other SOTA text and multi-modal web agents on this benchmark in most categories by 10-30\%. We then synthesize our learnings from the development of Agent-E into general design principles for developing agentic systems. These include the use of domain-specific primitive skills, the importance of distillation and de-noising of environmental observations, the advantages of a hierarchical architecture, and the role of agentic self-improvement to enhance agent efficiency and efficacy as the agent gathers experience.
Forward citations
Cited by 12 Pith papers
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MolmoWeb: Open Visual Web Agent and Open Data for the Open Web
Open 4B and 8B visual web agents achieve state-of-the-art results on browser benchmarks by predicting actions from screenshots and instructions, outperforming similar open models and some closed larger-model agents, w...
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new benchmark for self-evolving memory in LLM agents across task streams, with baseline ExpRAG and proposed ReMem method that integrates reasoning, actions, and memory updates for continual improvement.
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SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents
SecureWebArena is a new benchmark suite for holistic security evaluation of LVLM-based web agents using diverse simulated environments, attack taxonomies, and multi-layered failure analysis across reasoning, behavior,...
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WebRetriever: A Large-Scale Comprehensive Benchmark for Efficient Web Agent Evaluation
WebRetriever is a benchmark of 800 websites and 1,550 tasks with an automated evaluator (NavEval) achieving ~91–97% human agreement, showing current web agents succeed on only 11–37% of realistic tasks across three ev...
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Mango: Multi-Agent Web Navigation via Global-View Optimization
Mango raises web agent success rates to 63.6% on WebVoyager and 52.5% on WebWalkerQA by bandit-based starting-point selection and memory, beating baselines by 7.3% and 26.8%.
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and ...
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A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains
The paper proposes Amazon-Bench, a functionality-grounded benchmark for web agents in e-commerce that generates diverse task queries from webpage elements and evaluates both task performance and safety risks.
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ElephantAgent: Contextual State Continuity in Agentic Systems
ElephantAgent maintains a linearizable ledger of contextual state transitions via replicated trusted hardware and adds historical traceability for post-hoc recovery from semantic abuse in agentic systems.
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Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
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WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent
WebUncertainty improves web agent performance on benchmarks by adaptively selecting planning modes based on task uncertainty and using confidence-induced action uncertainty in MCTS to quantify aleatoric and epistemic ...
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Agentic Reasoning for Large Language Models
The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applicat...
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