Summary (Overview)

• This paper presents the first systematic survey on Agentic Environment Engineering for Large Language Models (LLMs), covering the full lifecycle of environment modeling, synthesis, evaluation, and application.
• The authors define agentic environments as dynamic, interactive systems designed for evaluating and training LLM agents, formalized as Partially Observable Markov Decision Processes (POMDPs).
• The paper introduces a comprehensive taxonomy of environments across 8 attributes (symbolic vs. neural, open-loop vs. closed-loop, online vs. offline, MDP vs. POMDP, deterministic vs. nondeterministic, discrete vs. continuous, unimodal vs. multimodal, single-agent vs. multi-agent) and 8 domains (GUI, Deep Research, Embodied, Game, Tool, Code, Domain-Specific, Cross-Domain).
• Two main paradigms for automated environment synthesis are identified: Symbolic Synthesis (code-driven, rule-based) and Neural Synthesis (world model-based, parameterized by neural networks).
• The paper characterizes agent-environment co-evolution through 4 agent evolution pathways (memory-centric, orchestration-centric, trajectory-centric offline, exploration-centric online) and 3 environment evolution paradigms (neural-driven, difficulty-driven, scaling-driven).

Introduction and Theoretical Foundation

The paper addresses a critical gap in LLM agent research: while environments serve as the interactive systems that drive agent capability evolution, existing work lacks systematic categorization and deep analysis of agentic environments.

Key Motivation:

• Real-world interactions for training agents are often infeasible due to high costs, safety risks, and data privacy concerns
• Real-world environments are irreproducible, making operational failures exceptionally costly
• Manually engineering simulated environments is resource-intensive and suffers from constrained scenario coverage
• Agentic environments address these challenges by incorporating sophisticated tools and reward signals that closely approximate real-world conditions

Theoretical Foundation: The paper formally defines the Environment $E$ as a Partially Observable Markov Decision Process (POMDP), represented by the tuple $\langle S, A, P, R, \Omega, O, \gamma \rangle$:

• State Space $S$: The set of all possible latent states of the environment
• Action Space $A$: The set of all possible actions available to the agent
• Transition Function $P$: $S \times A \rightarrow \Delta(S)$, governing the environment's physical dynamics
• Reward Function $R$: $S \times A \rightarrow \mathbb{R}$, providing scalar feedback $r_t$
• Observation Space $\Omega$ and Observation Function $O$: $S \times A \rightarrow \Delta(\Omega)$, the agent's perception of the latent state
• Discount Factor $\gamma$: A constant $\in [0,1)$

The Agent is defined as an autonomous decision-making entity with policy $\pi: H \rightarrow \Delta(A)$, where $H$ is the history space. The agent's objective is to maximize the Expected Discounted Return:

$$J(\pi) = \mathbb{E}_{\pi, P, O}\left[\sum_{k=0}^{\infty} \gamma^k r_{t+k}\right]$$

Key Shift from Data Engineering to Environment Engineering:

1. From Passive Learning to Collaborative Evolution: Environments dynamically adjust task complexity based on agent performance, unlike static datasets
2. From Single-turn Q&A to Multi-turn Interaction: Environments support multi-turn engagement and external tool integration
3. From Open-loop System to Closed-loop System: Environments establish continuous coupling between agent actions and resulting state changes

Methodology

The paper organizes its analysis around three research questions (RQ1-RQ3) and employs a systematic literature review methodology.

Environment Attribute Analysis (RQ1)

The paper defines 8 attribute pairs for characterizing environments:

Environment Domain Classification

The paper categorizes environments into 8 domains:

1. GUI: Desktop GUI (OSWorld, WindowsAgentArena), Mobile GUI (AitW, AndroidWorld), Web GUI (WebShop, WebArena, VisualWebArena)
2. Deep Research: Information Search (SimpleQA, WideSearch), Multi-Source Reasoning (GAIA, BrowseComp), Research Report Writing (DeepResearch Bench, DR.BENCH)
3. Embodied: Spatial Navigation (Habitat, Room-to-Room), Physical Manipulation (RLBench, Robocasa), Long-Horizon Planning (ALFRED, ALFWorld)
4. Game: Open World Games (MineDojo), Puzzle Reasoning Games (Baba Is AI), Social Deduction Games (AvalonBench, Werewolf), Adventure Quest Games (BALROG, GameArena), Strategy Management Games (CivRealm, Factorio Learning Environment)
5. Tool: Conventional Tool Use (API-Bank, ToolBench), User-Simulated Tool Use ($\tau$-bench), MCP-based Tool Use (MCPVerse, MCP-Bench)
6. Code: Code Generation, Code Understanding, Code Verification, Code Debugging (SWE-Bench, InterCode)
7. Domain-Specific: Biomedical/Healthcare, Science/Technology, Finance/Investment
8. Cross-Domain: OpenAI Gym, AgentBench, GEM

Environment Synthesis Methods (RQ2)

Symbolic Synthesis (code-driven environments):

• Task-Driven Synthesis: Environments built from predefined task specifications (e.g., SWE-Gym, Text2World, WorldCoder)
• Real-World-Driven Synthesis: Environments constructed from real-world data sources (e.g., AgentSynth, EnvGen, OSWorld-MCP)
• De Novo Synthesis: Environments generated from scratch without predefined templates (e.g., AutoEnv, LOGIGEN, InfiniteWeb)

Neural Synthesis (model-driven environments):

• Pixel-Level Modeling: Neural networks predict pixel-level state transitions (e.g., EVA, DIAMOND, NeuralOS, Cosmos)
• Word-Level Modeling: LLMs simulate environment dynamics through text generation (e.g., RAP, WebDreamer, CWM, SWE-World)
• Latent-Level Modeling: Neural networks operate in learned latent spaces (e.g., V-JEPA 2, I-JEPA, DINO-world, DINO-Foresight)

Environment Evaluation

The paper discusses quality control across four dimensions:

• Correctness: Well-established with existing evaluation frameworks
• Diversity: Under-researched but crucial for broad agent training
• Complexity: Under-researched but important for progressive learning
• Fidelity: Under-researched but essential for sim-to-real transfer

Empirical Validation / Results

The paper provides extensive tables cataloging environments across domains. Key findings include:

Table 1: GUI and Deep Research Environments (partial excerpt)

Table 2: Embodied and Game Environments (partial excerpt)

Key Observations:

• Most existing environments are single-agent (multi-agent environments are underrepresented)
• The majority operate in partially observable settings
• Discrete action spaces dominate, with fewer continuous action space environments
• Online interaction is the predominant paradigm
• Text-only environments are common, but multimodal environments (text+image) are growing

Agent Evolution Pathways

The paper identifies 4 agent evolution categories:

1. Memory-Centric Experience Evolution: Accumulating and leveraging past interaction experiences

   • Instance Trajectory Experience (e.g., OpenAgent, CoPS)
   • Abstract Scripts Experience (e.g., Reasoning Bank, Agent-Pro)
   • Structured Skill Experience (e.g., SAGE, SkillWeaver, SkillRL)

2. Orchestration-Centric Workflow Evolution: Dynamic coordination of task sequences

   • Fixed Workflow (e.g., MetaGPT, SWE-agent)
   • Automated Workflow (e.g., HuggingGPT, AutoFlow)
   • Evolving Workflow (e.g., AFlow, DSPy, GPTSwarm)

3. Trajectory-Centric Offline Evolution: Generating high-quality trajectories for supervised fine-tuning

   • Task Synthesis (e.g., OS-Genesis, Insta)
   • Trajectory Synthesis (e.g., ToolAlpaca, Aguvis)
   • Trajectory Refinement (e.g., Toolformer, ETO, Self-Improvement)

4. Exploration-Centric Online Evolution: Using reinforcement learning for dynamic adaptation

   • Reasoning Structure (e.g., DeepRetrieval, Search-R1)
   • Reward Shaping (e.g., Agent-R1, ToolRL, GDPO)
   • Algorithmic Optimization (e.g., RAGEN, GiGPO, ARPO)

Environment Evolution Paradigms

1. Neural-Driven Evolution: Adjusting environment parameters via neural networks

   • Self-Play (e.g., Absolute Zero, Self-Challenging, Active Zero)
   • World Model (e.g., WebDreamer, UI-Simulator, Code2World)

2. Difficulty-Driven Evolution: Adapting task complexity to match agent capabilities

   • Explicit Curriculum Signals (e.g., POET, AgentGen, SEC)
   • Implicit Curriculum Mechanisms (e.g., PAIRED, DCD, ACCEL, EnvGen)

3. Scaling-Driven Evolution: Expanding environment diversity and scope

   • Scenario-Level Scaling (e.g., FTRL, AgentScaler, AutoForge)
   • Environment-Level Scaling (e.g., ARE, AutoEnv)

Theoretical and Practical Implications

Theoretical Implications:

• The paper establishes a formal POMDP-based framework for agentic environments, providing a unified mathematical foundation for future research
• The distinction between data engineering and environment engineering highlights a fundamental paradigm shift in how LLM agents should be trained and evaluated
• The identification of environment attributes (8 pairs) provides a systematic framework for analyzing and comparing environments
• The concept of Environment-Agent Alignment formalizes the optimization objective of maximizing expected returns through environment interaction

Practical Implications:

• Environment-as-a-Service (EaaS): The paper proposes a paradigm for standardized, scalable, and reproducible environment deployment
• Multi-Agent Environments: Future designs should incorporate cooperative and competitive multi-agent settings to realize collective intelligence
• Neural-Symbolic Environments: Current agents face a deployment bottleneck between rigid symbolic systems and uncontrollable neural models; future infrastructures must bridge this gap
• Sim-to-Real Alignment: Environments must better approximate real-world conditions for reliable agent training
• Agent-Environment Co-Evolution: The closed-loop interaction between agent and environment evolution is crucial for continuous improvement

Key Theoretical Contributions:

• The paper introduces Agentic Reinforcement Learning algorithms (PPO, GRPO, DAPO) as foundational methods for environment-based agent training
• The GRPO objective eliminates the need for an explicit value network: $$\mathcal{L}_{GRPO}(\theta) = \mathbb{E}_q\left[\frac{1}{G}\sum_{i=1}^G \frac{1}{|y_i|}\sum_{t=1}^{|y_i|}\left(\mathcal{L}_{i,t}^{CLIP}(\theta) - \beta D_{KL}(\pi_\theta \|\pi_{ref})\right)\right]$$
• The DAPO objective introduces decoupled clipping for more stable optimization: $$\mathcal{L}_{i,t}^{D-CLIP}(\theta) = \min\left(r_{i,t}(\theta)\hat{A}_i, \text{clip}(r_{i,t}(\theta), 1-\epsilon_{low}, 1+\epsilon_{high})\hat{A}_i\right)$$

Conclusion

Main Takeaways:

1. Agentic environments are essential for evaluating and training LLM agents, providing interactive systems that enable multi-turn engagement, tool integration, and closed-loop feedback
2. The paper provides a comprehensive taxonomy covering 8 environment attributes and 8 domains, offering a systematic framework for analyzing and comparing environments
3. Two main synthesis paradigms exist: symbolic (code-driven, reliable but limited scalability) and neural (model-driven, scalable but less controllable)
4. Agent-environment co-evolution follows four agent evolution pathways and three environment evolution paradigms
5. Environment quality evaluation across correctness, diversity, complexity, and fidelity remains an under-researched area

Future Directions:

1. Environment-as-a-Service: Standardized, scalable, and reproducible environment deployment
2. Multi-Agent Environments: Cooperative and competitive settings for collective intelligence
3. Neural-Symbolic Environments: Combining the reliability of symbolic systems with the scalability of neural models
4. Dynamic, Long-Horizon, Open-Ended Environments: Requiring fundamentally new agent capabilities
5. Sim-to-Real Alignment: Ensuring environments accurately approximate real-world conditions
6. Environment Scaling Laws: Establishing scientific foundations for understanding how environment size and complexity relate to agent capability development
7. Environment-Capability Relationships: Understanding how different environment properties affect the development of specific agent capabilities

Critical Open Challenges:

• Evaluating diversity, complexity, and fidelity of environments remains under-researched
• Most existing frameworks are limited to single-agent scenarios
• Current agents face a deployment bottleneck between rigid symbolic frameworks and fully generative world models
• The field lacks systematic understanding of how environment properties scale with agent capabilities