Proxy-Execution Agent (PEA)

• Proxy-Execution Agent (PEA) is a specialized software module that translates abstract execution requests into concrete, verifiable actions in multi-agent systems.
• It enforces policy compliance through dynamic risk assessment and employs static safety checks, including containerized execution for high-risk operations.
• Empirical results show that PEAs enhance system efficiency and reliability by enabling secure context management and reducing resource overhead in ML and protocol-driven environments.

A Proxy-Execution Agent (PEA) is a specialized software or agentic module that mediates and governs the execution of actions, tool invocations, or protocols on behalf of other agents, clients, or higher-order controllers, typically in multi-agent systems, tool-augmented LLM platforms, or collaborative machine learning environments. PEAs play a key role in decoupling high-level planning or reasoning from concrete execution, enforcing policy-compliant, secure, and resource-aware translation of abstract execution requests into verifiable action outcomes. Rigorous variants of the PEA concept provide static safety guarantees, dynamic policy enforcement, and fidelity-aware proxying as surveyed in multi-agent protocol extension, tool-augmented reasoning, and large-scale ML engineering systems (Silva et al., 2014, Ahmadi et al., 11 Apr 2025, Molinari et al., 3 Dec 2025, Yuan et al., 6 Nov 2025).

1. Formal Definitions and Core Mechanisms

PEAs are defined by their mediation of the execution path between high-level invokers and execution backends or protocols. In Model Context Protocol (MCP) environments, a PEA is a software layer that exposes a uniform API, accepts execution requests, evaluates request risk or policy, forwards requests to appropriate tool servers via supported transports (e.g., STDIO, SSE, or REST), and returns results or confirmation challenges as prescribed by the risk model (Ahmadi et al., 11 Apr 2025). Formally, given:

• $C$: client requests,
• $S$: MCP server instances,
• $T$: supported transports,
• $\rho: S \times \mathrm{Tool} \rightarrow \{1,2,3\}$: the risk-level assignment,
• $\mathcal{E}_\ell$: level-$\ell$ execution operator,

the PEA is a function: $$\mathrm{PEA}:\;C \to \begin{cases} \mathcal{E}_1(s,\tau,q) & \text{if }\rho(s,q)=1 \ \text{confirmation challenge} & \text{if }\rho(s,q)=2 \ \mathcal{E}_3^{\mathrm{docker}}(s,\tau,q) & \text{if }\rho(s,q)=3 \end{cases}$$ for target server $s$, transport $\tau$, and tool call $q$.

In the context of agent protocol extension, PEAs can dynamically load new interaction protocols, statically verify them for safety (syntactic, resource, privacy), and then execute them under a labeled transition system derived from extended Petri nets (Silva et al., 2014). For ReAct-augmented LLM systems, the PEA decomposes the “Think → Act → Observe” cycle into a low-level, micro-task–oriented execution module, incorporating context-window management and tool-output offloading (Molinari et al., 3 Dec 2025).

2. Architectural Patterns and Implementation Variants

PEAs implement diverse architectural patterns, matched to their system and domain:

System	Request Input	Execution Logic	Output Handling
MCP Bridge (Ahmadi et al., 11 Apr 2025)	REST API, MCP JSON-RPC	Risk-based, level-triggered forwarding	Result/confirmation, REST response
RP-ReAct (Molinari et al., 3 Dec 2025)	Sub-question tag (RPA)	ReAct loop + context-saving	Answer, observation, proxy variable
ArchPilot (Yuan et al., 6 Nov 2025)	PyTorch code fragment	Proxy-/full-train orchestrator	Proxy vector, score, y(c) if full
Open MAS (Silva et al., 2014)	Protocol description	Petri-net verifier + executor	Protocol outcome, error state

• In MCP Bridge, a PEA adds a RESTful proxy layer between client and MCP servers, including a risk policy, confirmation logic, and Docker-based isolation (Ahmadi et al., 11 Apr 2025).
• In RP-ReAct, the PEA is an LLM-driven micro-agent handling only tool-level interaction; it manages its local context, offloads voluminous outputs, and maintains robust tool execution traces (Molinari et al., 3 Dec 2025).
• In ML engineering contexts such as ArchPilot, the PEA serves as both instrumented trainer and proxy-function aggregator, fitting a fidelity-aligned proxy-weighted metric to steer adaptive search (Yuan et al., 6 Nov 2025).
• In open multi-agent systems, PEAs load and verify Petri-net-encoded protocols, enforcing safety through static analysis and trust checks prior to execution (Silva et al., 2014).

3. Risk and Safety Models

Risk assessment and policy enforcement are central PEA functions. In MCP Bridge, each tool or server is assigned a discrete risk level $\ell\in\{1,2,3\}$, configuring the execution pathway:

• Level 1: standard execution, no extra checks.
• Level 2: confirmation workflow requiring explicit approval before execution.
• Level 3: strict containerized (Docker) execution with cgroups, seccomp, and network namespace isolation (Ahmadi et al., 11 Apr 2025).

For Petri-net PEAs, static safety analysis must ensure:

• Syntactic/semantic protocol validity.
• Absence of unbounded execution or starvation cycles.
• Privacy property: every action accessing private data is guarded by both past and future preconditions (Silva et al., 2014).

In multi-agent LLM environments, context-saving and output offloading serve to protect against context-window overflow, ensuring that complex tool outputs do not jeopardize reasoning quality or privacy (Molinari et al., 3 Dec 2025).

4. Coordination, Messaging, and Context Management

PEAs mediate protocol translation, state management, and context synchronization with upstream controllers:

• In Reason-Plan-ReAct, the Reasoner-Planner Agent emits abstract sub-questions, and the PEA—via a well-scoped prompt and schema—translates these into concrete tool-invocation trajectories. Context-saving logic is embedded to cap onboard tokens, offload complete outputs externally, and signal result availability via variable referencing (Molinari et al., 3 Dec 2025).
• In protocol-extendable MAS, PEAs communicate protocol acceptance/rejection, guarantee requests (for precondition fulfillment), and, upon verification, execute protocol transitions per dynamic message and marking state (Silva et al., 2014).
• In the MCP Bridge paradigm, the PEA exposes REST endpoints that unify heterogeneous server backends, and provides both synchronous process execution and stream/event-driven semantics (Ahmadi et al., 11 Apr 2025).

5. Proxy Functions, Scoring, and Adaptive Aggregation

PEAs operating in proxy-based evaluation regimes leverage multiple low-cost “proxy” metrics to estimate or filter candidates at scale. In ArchPilot:

• The PEA executes a registry of proxy functions (e.g., one-epoch validation loss, noisy accuracy, feature-dropout robustness).
• Each proxy’s output is scaled by a direction alignment coefficient (to standardize on “higher is better”) and weighted by a nonnegative vector $\lambda \in \Delta$.
• The fidelity-aware score is

$s(c) = \sum_{i=1}^m \lambda_i d_i x_i(c)$

with $\sum_i \lambda_i = 1$, $\lambda_i \geq 0$.

• Proxy weights are fitted via ridge-regularized least squares to maximize alignment to “true” full-train outcomes (Yuan et al., 6 Nov 2025).
• The PEA manages the proxy registry, implements hard-zero removal policies for failed proxies, and triggers rescore/restart of the global search tree when aggregate fidelity shifts.

6. Empirical Performance, Limitations, and Future Development

Empirical findings demonstrate that PEAs enhance efficiency, robustness, and scalability:

• MCP Bridge achieves cross-platform compatibility, risk-tunable isolation, and enables LLM-powered tool use on environments previously incapable of local MCP hosting. However, it currently lacks detailed quantitative latency/throughput benchmarks and penetration-test security metrics (Ahmadi et al., 11 Apr 2025).
• RP-ReAct (with PEA context-saving) empirically improves trajectory stability and context management on complex multi-tool tasks, with qualitative evidence for reduced context exhaustion and improved reliability over ReAct baselines. Combined performance scores (CPS) and accuracy demonstrate robustness across six open-weight models (Molinari et al., 3 Dec 2025).
• In ML engineering, the ArchPilot PEA enables resource-efficient candidate selection by proxy training, achieving a valid-submission rate of 0.893 and reaching baseline performance with 20–30% less GPU time on average (Yuan et al., 6 Nov 2025).
• Open MAS PEAs statically verify dynamic protocol importation, providing runtime safety and privacy guarantees, but require maintenance of a trust store and protocol compatibility enforcement (Silva et al., 2014).

Identified limitations and future directions include:

• Fine-grained access control and OAuth/OpenID integration for MCP Bridge PEAs (Ahmadi et al., 11 Apr 2025).
• Advanced proxy/summarization methods for PEA context optimization (Molinari et al., 3 Dec 2025).
• Distillation of PEA logic into compact, on-device LLMs, and full quantitative measurements on latency, throughput, and system load across all PEA paradigms.

7. Theoretical and Practical Significance

PEAs enable modular, policy-compliant execution in agentic and tool-augmented AI, bridging the gap between abstract decision-making and reliable, context-safe action. Their formulation allows decoupled evolution of protocol and tool logic, tuned isolation and safety guarantees, scalable orchestration under resource constraints, and empirical advances in robustness and efficiency across diverse domains including protocol-centric MAS, enterprise toolchains, and large-scale ML engineering (Silva et al., 2014, Ahmadi et al., 11 Apr 2025, Molinari et al., 3 Dec 2025, Yuan et al., 6 Nov 2025).