AgentLess Systems Overview

• AgentLess is a framework where external orchestration replaces in-guest agents, reducing operational overhead and mitigating security risks.
• It has demonstrated significant performance improvements in virtual machine security and bug fixing by employing hypervisor-level monitoring and fixed pipelines.
• Hybrid approaches integrating agentless and agent-based methods are emerging to address the limitations of static methodologies and improve dynamic adaptability.

AgentLess refers to a class of systems, methodologies, and frameworks in which autonomous behaviors, diagnostics, monitoring, or repair are achieved without deploying a software agent or persistent script inside the guest environment. In contrast to agent-based systems—which incorporate internal agents for dynamic decision-making, interactive action loops, or runtime instrumentation—agentless solutions execute control, analysis, and management externally (often at the hypervisor, orchestration layer, or with a fixed pipeline), thereby enhancing security, transparency, and simplicity. This paradigm encompasses diverse applications including virtual machine security monitoring, malware sandboxing, automated code repair, repository-level software engineering, general bug reproduction, and performance optimization.

1. AgentLess Paradigms: Definition, Rationale, and Historical Evolution

The agentless paradigm emerged as an answer to the limitations imposed by traditional agent deployment—operational overhead, security exposure, and ease of detection or subversion by adversarial entities. In virtual machine security, agentless frameworks monitor guest filesystems from the hypervisor or host domain, ensuring that no monitoring code runs inside the potentially compromised guest (Zhan et al., 2018). In software engineering and automated bug fixing, agentless workflows restrict the autonomy of LLMs, employing static, pre-defined sequences for localization, patching, and validation, rather than delegating tool invocation and iterative environment interaction to the LLM itself (Xia et al., 1 Jul 2024, Meng et al., 15 Nov 2024).

In cybersecurity, agentless sandboxes operate by embedding behavioral monitors at the hypervisor level, rather than instantiating analysis agents within the target system, thereby improving resistance to anti-analysis strategies deployed by sophisticated malware (Ali et al., 2019). The progression toward “agentless” approaches is motivated by both theoretical and practical concerns: reducing attack surface, improving auditability, and—recently—mitigating the error-propagation risks and cognitive deadlocks seen in long-agentic tool chains (Liu et al., 17 Sep 2025).

2. Key AgentLess Methodologies

2.1 Virtual Machine Filesystem Security

Agentless periodic monitors minimize the scan set in every poll by only checking files newly created or modified since the previous scan (Zhan et al., 2018). For single image VMs, a backend disk driver intercepts write operations and accumulates a “dirty block map”:

$$\text{block\_address} = \frac{\text{offset} - \text{header}}{4096}$$

Only blocks marked dirty trigger further integrity or virus scans, eliminating redundant file access. For VMs using copy-on-write (QCOW2) overlays, modification detection leverages analysis of overlay allocations to localize changes, with the scan restricted to those overlays rather than the base image.

2.2 Malware Behavioral Analysis

Agentless sandboxing solutions instrument at the hypervisor or lower, capturing guest VM activity (system calls, process execution) without deploying agents visible to the malware (Ali et al., 2019). The feature extraction can utilize n-gram modeling over API call sequences:

$$g_i = (a_i, a_{i + 1}, ..., a_{i + k - 1}), \quad v \in \{0, 1\}^m$$

This method yields behavioral signatures robust to agent-detection countermeasures inherent in advanced malware.

2.3 LLM-based Software Engineering

Agentless frameworks such as Agentless (Xia et al., 1 Jul 2024) and MagentLess (Zan et al., 3 Apr 2025) apply structured, deterministic stage-based processes for bug fixing and issue resolving:

• Localization: Hierarchical narrowing from file-level to statement/function-level via repository skeletons or full file content parsing (often using Tree-sitter for multilingual adaptation).
• Repair: LLMs accept reduced context and produce candidate diff-format patches.
• Validation: Patches are filtered by syntax and regression checks, with normalization and majority voting for candidate selection.

No autonomous action planning, tool invocation, or environment feedback loop is permitted; the pipeline is fixed and external to the model’s interactive capabilities.

3. Performance, Accuracy, and Comparative Evaluation

Agentless approaches have demonstrated efficacy, with strengths in cost efficiency and stability. For filesystem monitoring on cloud VMs, agentless systems reduced file scan time by orders of magnitude, e.g., scanning only 5 of 10,000 files per hour on Linux, and typical anti-virus scan times were reduced from 273 seconds to under 20 seconds on Windows images (Zhan et al., 2018). In bug fixing, Agentless achieved 32.00% fix rate and lowest reported cost ($0.70) on SWE-bench Lite, outperforming more complex agent-based open-source tools (Xia et al., 1 Jul 2024).

In multilingual issue-resolving, MagentLess adaptation offered competitive “single-file fix” rates, although more complex cross-file scenarios favored agentic interaction (Zan et al., 3 Apr 2025). Empirical studies reveal that agentless models are less prone to long-tail cognitive deadlocks and failed iteration loops but are more cost-sensitive and vulnerable to early-stage localization errors (Liu et al., 17 Sep 2025, Meng et al., 15 Nov 2024).

In code performance optimization (SWE-Perf), Agentless secured high patch application rates (88.57%) but achieved only minimal runtime improvements (0.41%), falling behind agentic methods such as OpenHands (2.26%) and expert-level baselines (10.85%) (He et al., 16 Jul 2025). In AutoExperiment, a research-code benchmark, the fixed, agentless harness produced lower pass rates (e.g., 8.3% vs. 35.3% for interactive agents with GPT-4o), highlighting a critical performance gap in long-horizon, context-sensitive generation (Kim et al., 24 Jun 2025).

4. Hybridization, Enhancement, and AgentLess Extensions

Recent developments indicate that hybrid approaches or augmentation strategies can overcome agentless limitations in localization, reasoning, and execution. RepoLens enhances AgentLess by extracting and clustering conceptual concerns from code bases, then integrating them into localization prompts. Using a multi-stage similarity and clustering metric,

$$\text{Sim}(nt_i, nt_j) = \frac{\text{name\_sim} + \text{def\_sim} + \text{func\_sim} + \text{call\_bonus}}{4}$$

agentless workflows are refined for large-scale repositories, improving file-level Hit@1 from 15.28% to 29.17% (Wang et al., 25 Sep 2025).

BugCerberus introduces hierarchical localization across file, function, and statement levels. The system, powered by three customized LLMs, improves Agentless issue-fixing rates by 17.4%, demonstrating that fine-grained, model-based localization dramatically raises overall repair success (Chang et al., 21 Feb 2025).

AEGIS applies structured context extraction and finite-state-machine-driven script optimization to generate reliable bug reproduction scripts. When used to inform Agentless patch selection, the methodology achieves a 12.5% increase in resolved rate (Wang et al., 27 Nov 2024).

Hybrid agentless-agentic architectures, such as the Expert-Executor framework, leverage a supervisory agent for strategic oversight, while retaining the stability of a pipeline-based executor. This design corrects non-progressive iteration and flawed reasoning, yielding a 22.2% improvement over agentic baselines on problematic issues (Liu et al., 17 Sep 2025).

5. Limitations and Critical Analysis

Agentless methods are fundamentally limited by their rigidity: they lack dynamic environment interaction (e.g., debugging, multi-step verification) and are susceptible to early mislocalization. Comparative analyses (Meng et al., 15 Nov 2024, He et al., 16 Jul 2025, Kim et al., 24 Jun 2025) show that agentless approaches fall behind agentic systems for tasks requiring complex reasoning, adaptive repair, or long-context management. The absence of feedback-driven, iterative adaptation impedes deep performance tuning, reproduction scripts, and more intricate patch validation.

Additionally, in auto-experiment benchmarks, the gap between agentless and agentic (dynamic) systems increases rapidly as difficulty scales (more masked code, less context). While agentless pipelines are easier to analyze and more predictable, their performance bottlenecks suggest they are best deployed either in conjunction with agentic oversight or for relatively bounded tasks with clear structure and well-localized defects.

6. Future Directions and Implications

Ongoing work in the field suggests several directions. Integrating language-agnostic regression and reproduction testing into agentless pipelines may expand their applicability across ecosystems (Zan et al., 3 Apr 2025). Augmenting agentless architectures with domain-specific conceptual knowledge (e.g., RepoLens concerns) or hierarchical localization strategies (BugCerberus) offers a clear path to performance improvements, especially for large-scale or multi-language repositories (Chang et al., 21 Feb 2025, Wang et al., 25 Sep 2025).

Advances in graph-integrated LLMs, such as Code Graph Model (CGM), indicate that agentless frameworks can further scale by exploiting code structure and semantic dependencies—CGM achieves 43.00% resolution rate in SWE-bench Lite, surpassing previous open-source systems (Tao et al., 22 May 2025).

A plausible implication is that hybrid systems, combining agentless precision and agentic adaptability, may yield the most robust, interpretable, and scalable solutions for future autonomous diagnostics, repair, and optimization tasks. As research agents grow increasingly sophisticated, benchmarking agentless methods remains vital as a baseline and control in the evaluation of agentic advances (Xia et al., 1 Jul 2024, Kim et al., 24 Jun 2025).

Table: AgentLess Approaches and Domains

Domain	Typical AgentLess Methodology	Noted Strength
VM Security Monitoring	Hypervisor-based monitoring, dirty block map	External to guest; low overhead
Malware Analysis	Hypervisor-level sandboxing	Resist anti-analysis; richer features
Automated Bug Fixing	Deterministic pipeline: localization, repair, validation	Cost-efficient; interpretable
Multilingual Issue Resolving	MagentLess: Tree-sitter, file pruning	Scalability; controlled adaptation
Performance Optimization	Hierarchical localization, pipeline patch selection	High apply rates; limited perf. gain
Research Experiment Replication	Fixed-action harness, no interaction	Baseline utility; limited robustness

AgentLess frameworks constitute an essential architectural alternative in secure monitoring, cyber-defense, and automated software engineering. Their design philosophy prioritizes external orchestration, predictability, and transparency, with current research focusing on hybrid systems that leverage both agentless advantages and the interactive problem-solving power of agentic tools.