MEnvAgent: Multi-Agent Environment Optimization

Updated 3 February 2026

MEnvAgent is a suite of autonomous multi-agent systems designed for scalable and verifiable environment construction and optimization in both physical and digital domains.
It leverages formal methodologies including MDPs and Dec-POMDPs alongside deep RL, CNNs, and GNNs to achieve decentralized control and efficient environment synthesis.
Empirical benchmarks demonstrate significant gains in energy savings, build time reduction, and performance improvements in diverse applications such as physical control and software engineering.

MEnvAgent refers to a suite of multi-agent systems and formal methodologies designed for autonomous, scalable, and verifiable environment construction, configuration, and optimization. The term appears in several key contexts, notably distributed control of physical environments (e.g., data center chiller systems), collaborative environment optimization in multi-agent navigation, and, most recently, in software engineering for polyglot executable environment generation. Across these applications, MEnvAgent frameworks embody decentralized intelligence, model-based planning, robust feedback, and resource-efficient execution, unified by rigorous mathematical formalization and empirical validation (Astudillo et al., 21 Feb 2025, Gao et al., 2022, Guo et al., 30 Jan 2026, Guo et al., 23 Jan 2026).

1. Core Definitions and Primary Instantiations

MEnvAgent, as a generic term, encapsulates an autonomous agent responsible for environment-centric tasks, either cyber-physical (e.g., thermal regulation) or digital (e.g., Docker environment synthesis). In physical control settings, the MEnvAgent is a Local Deep Reinforcement Learning (RL) entity deployed to edge hardware, integrating local sensing, decentralized decision making, peer-to-peer coordination, and reporting to a central aggregator (Astudillo et al., 21 Feb 2025). In software engineering, MEnvAgent denotes a multi-agent orchestration for verifiable software environment construction, combining specialized planning, execution, and verification sub-agents in a closed feedback loop (Guo et al., 30 Jan 2026).

2. Mathematical Modeling and Algorithmic Architectures

MEnvAgent frameworks are grounded in Markov Decision Processes (MDPs) or, in multi-agent derivatives, Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs). A canonical physical MEnvAgent is modeled as: $\mathcal{M} = (\mathcal{S}, \mathcal{A}, P, R, \gamma)$ with state vectors comprising relevant environment variables (e.g., temperature, humidity, chiller load, weather) and continuous action spaces (e.g., chiller adjustments) (Astudillo et al., 21 Feb 2025). The agent's policy $\pi_\theta(a_t | s_t)$ is optimized for cumulative discounted reward, with losses and gradients computed as: $J(\theta) = \mathbb{E}_{\pi_\theta}\Big[\sum_{t=0}^\infty \gamma^t R(s_t,a_t)\Big]$ and

$\nabla_\theta J \approx \mathbb{E}[\nabla_\theta \log \pi_\theta(a_t|s_t) Q(s_t,a_t)]$

In the setting of multi-agent environment design, e.g., obstacle arrangement for navigation, the MEnvAgent is formalized as a policy $\pi_o$ acting over joint agent-environment states, maximizing a bi-level constrained objective which incorporates both agent performance and environment modification cost (Gao et al., 2022). These architectures leverage CNNs or GNNs for state encoding and distributed control.

In verifiable software engineering, MEnvAgent systems split the construction and validation workflow across specialty sub-agents. Each agent (e.g., Repository Analysis, Environment Setup, Test Configuration, Environment Execution, Verification) acts sequentially and iteratively on a shared workspace, with feedback loops ensuring progression towards a verifiable PASS/F2P state (Guo et al., 30 Jan 2026).

3. Communication, Coordination, and Reuse Protocols

Physical MEnvAgents coordinate via lightweight message buses (MQTT, OPC-UA), broadcasting heartbeat/status and proposed action messages, and engaging in Metropolis–Hastings-weighted consensus rounds to avoid conflicting control policies among neighbors: $x_i^{(k+1)} = w_{ii} x_i^{(k)} + \sum_{j\in\mathcal{N}_i} w_{ij} x_j^{(k)}$ where $w_{ij}$ are weights satisfying stochastic normalization (Astudillo et al., 21 Feb 2025).

In digital MEnvAgent systems for software engineering, communication follows modular, fine-grained protocols. Agents emit summaries, requests, and logs via structured channels, and loop until a verifiable environment is synthesized. A critical innovation is the Environment Reuse Mechanism: instead of full scratch builds, the agent retrieves a historical environment $S_{sim}$ and applies minimal patches $\Delta \mathcal{P}$ to efficiently adapt to the new code snapshot, observed to reduce time cost by approximately 46% (Guo et al., 30 Jan 2026).

4. Evaluation Metrics and Empirical Benchmarks

Quantitative assessments of MEnvAgent systems are scenario-specific:

Distributed Physical Control: Metrics include mean energy savings (typically 8–15% compared to local baselines), improved fault-handling latency (detecting anomalies 30–40% faster), and mean time between failures (extended by ≈ 30%) (Astudillo et al., 21 Feb 2025).
Navigation and Environment Optimization: Metrics such as success-weighted path length (SPL, improved from ≈0.5 to ≈0.9), mean speed, and energy (obstacle movement) are reported. RL-based MEnvAgent policies outperform heuristic baselines, and the framework supports both offline (centralized CNN) and online (decentralized GNN) implementation (Gao et al., 2022).
Verifiable Software Environment Construction: Pass rate, Fail-to-Pass (F2P) rate, and mean time cost are primary metrics. On MEnvBench, MEnvAgent achieves F2P gains of +8.6%, pass rate improvements of +11.0%, and a 43% reduction in build time versus the strongest baseline (Guo et al., 30 Jan 2026).

Datasets such as MEnvBench and MEnvData-SWE (comprising thousands of polyglot, verifiable instances) provide reproducible, large-scale evaluation resources, enabling consistent performance gains in downstream agent tasks.

5. Limitations, Open Challenges, and Prospective Extensions

MEnvAgent frameworks face category-specific challenges:

Physical Environments: As agent count $N$ increases, consensus-induced latency can hinder scalability; solutions include hierarchical clustering and model compression for edge deployment. Variability in exogenous factors demands continual or federated re-training and secure, encrypted communication (Astudillo et al., 21 Feb 2025).
Software Engineering: Storage costs for maintaining historical environment pools, the need for language/toolchain extensibility, and safety/isolation against potentially malicious code are principal concerns (Guo et al., 30 Jan 2026).
Dynamic Prioritization and Diagnosis: Self-evolving frameworks, as in EvoConfig, introduce online priority adjustment for error correction but require sufficient error signal to stabilize and remain sensitive to resource constraints (Guo et al., 23 Jan 2026).

Future directions include hybrid symbolic-neural control, meta-learning for adaptive reuse cost ranking, integration of verifiable rewards in RL, coverage of additional languages and domains, demand-response integration in smart grid scenarios, and further refinement of error diagnosis and test-outcome reasoning.

MEnvAgent design reflects a cross-disciplinary convergence—unifying RL-based distributed control (Astudillo et al., 21 Feb 2025), environment co-design for multi-agent systems (Gao et al., 2022), and autonomous infrastructure for scalable, verifiable software engineering (Guo et al., 30 Jan 2026, Guo et al., 23 Jan 2026). Its principled, modular architectures, efficiency gains, and empirical advances delineate a framework of growing prominence in both physical systems management and automated AI-driven software workflows. The deployment of MEnvAgent and its dataset artifacts sets new standards for research reproducibility, robust evaluation, and generalization across previously intractable multilingual and cyber-physical configuration problems.