TermiAgent: Multi-Domain Agent Framework

• TermiAgent is a framework of modular, multi-agent systems that use advanced NLP, deep learning, and reasoning to extract and structure domain-specific terminology.
• It leverages methodologies like contextual clustering, flexible-equality term recognition, and Located Memory Activation for robust performance across varied applications.
• The framework is applied in penetration testing, legal auditing, and technology monitoring, demonstrating increased efficiency, reproducibility, and adaptable system interaction.

TermiAgent is a generic term adopted in recent research to describe agent-oriented frameworks designed for automated, context-sensitive, and domain-specific information extraction and system interaction, particularly in the fields of terminology processing, technology monitoring, legal policy analysis, and penetration testing. The term references a family of modular, often multi-agent, systems that operationalize advanced natural language processing, reasoning architectures, and domain-adapted benchmarks. TermiAgent systems represent an overview of statistical techniques, deep learning models, structured memory, and rules-based logic that enable environmental impact, goal-directed planning, and persistent state awareness.

1. Historical Origins and Conceptual Development

Early agent frameworks defined “agents” through the actor model and multi-agent architectures of the late 20th century. Definitions evolved from basic reflexive or goal-driven entities to complex, adaptive digital actors featuring autonomy, learning, and environmental interaction (Bent, 7 Aug 2025). In recent years, as LLMs have become central to the implementation of AI agents, the term “agent” has been diluted, prompting proposals for rigorous redefinition and standardization. Under this framework, TermiAgent must satisfy minimum criteria: producing active, measurable environmental impact, exhibiting goal-directed behavior with defined objectives, and maintaining persistent state across interactions.

TermiAgent systems are distinguished from simple input/output automata by their multidimensional agentic spectrum, encompassing environmental interaction sophistication, goal complexity, temporal coherence, learning/adaptation, and autonomy. This model allows research clarity, reproducibility, and precise policy formulation.

2. Terminology Processing and Extraction Architectures

TermiAgent frameworks often incorporate advanced terminology processing modules developed for extracting, recognizing, and structuring terms from corpora (Enguehard et al., 2014, Hossari et al., 2018). The IRIN Institute’s ACABIT module applies morphosyntactic pattern-matching and shallow parsing to pre-tagged corpora, extracting multi-word terms via regular expressions, local grammars, and co-occurrence-based association measures. ANA, an incremental term acquisition system, discovers terms from untagged corpora via context analysis and bootstrap expansion.

Flexible-equality term recognition is a core function, with the minimum editing distance formula:

$$WD(w, w') = \frac{\text{dist}(w, w')}{|w|+|w'|}, \quad w \sim w' \Leftrightarrow WD(w, w') \le \frac{1}{k}$$

This operator, extended to compound terms via segmentation, functional word removal, and positional averaging, enables robust normalization across morphological variants and spelling differences.

TermiAgent architectures also include term structuring via clustering (contextual or distributional similarity) and semantic linkage (hypernymy, synonymy, and cause–effect relations), as implemented by systems such as Promethee, leveraging lexico-syntactic patterns and iterative relation extraction.

3. Multi-Agent Systems for Penetration Testing

TermiAgent is instantiated in security domains as a multi-agent automated penetration testing framework (Mai et al., 11 Sep 2025). Comprising Reasoner, Assistant, Executor, Memory, and Arsenal modules, the architecture divides penetration objectives (e.g., shell acquisition) into dynamic, stage-wise goals. The Reasoner module directs high-level planning, while the Assistant module converts abstract goals into concrete instructions, cross-referencing Memory for situational context and Arsenal for exploit strategy.

The Memory module implements a hierarchical Penetration Memory Tree and applies Located Memory Activation via backward traversal and role-based compression, overcoming large context limitations common in LLMs. The Arsenal module standardizes exploit retrieval through the Unified Exploit Descriptor, synthesizing code/environment dimensions into reproducible, plug-and-play modules.

In the TermiBench benchmark—comprising 510 hosts, 25 services, and 30 CVEs—TermiAgent exhibits superior autonomy, discrimination between benign and exploitable services, and efficient exploit execution. Quantitatively, TermiAgent attains up to eightfold more successful penetrations and operates at approximately 20% of the time and 10% of the financial cost of prior state-of-the-art agents.

4. Legal and Policy Document Parsing

TermiAgent systems have been adapted for legal domain tasks, notably the agentic parsing and auditing of Terms of Service (ToS) documents (Mridul et al., 16 May 2025). Here, the architecture is modular: a Term Parsing Agent extracts legally binding clauses with referenced source locations, the Term Verifier agent ensures veracity relative to the source, and the Accountability Planner agent constrains audit-check creation to realistic user scenarios. Each step is interpretable, traceable (e.g., via line numbers in structured outputs), and centrally employs LLMs for semantic chunking and hallucination minimization.

These systems replace traditional summarization with scaffolded, stepwise processing conducive to automated compliance auditing and policy oversight. The transformation of legalese into actionable, verifiable instructions improves both usability and enforceability, supporting regulatory transparency and ethical oversight.

5. Benchmarking and Evaluation

Robust benchmarking is essential to the TermiAgent paradigm. The TermiBench framework (Mai et al., 11 Sep 2025) advances real-world evaluation by simulating adversarial, noise-laden infrastructure through multi-service environments, escalating tiers of benign background services, and minimal prior knowledge conditions. Unlike capture-the-flag settings, TermiBench requires system control acquisition, directly mapping agentic performance to real-world security efficacy.

Performance metrics are presented in terms of F-score (for term extraction systems (Hossari et al., 2018)), success rate, execution time, and cost ratios (for penetration frameworks (Mai et al., 11 Sep 2025)). The high F-score of 0.93 for sentence classification and 0.96 for term extraction marks the TEST architecture as competitive. TermiAgent demonstrates superior system shell acquisition and high compatibility across both large-scale and resource-efficient LLMs.

6. Challenges, Limitations, and Future Directions

Challenges facing TermiAgent implementations include:

• Data imbalance and annotation ambiguity, particularly for terminology extraction in heterogeneous corpora (Hossari et al., 2018).
• Context forgetting and information dilution in LLMs, requiring advanced memory management strategies (e.g., Located Memory Activation) (Mai et al., 11 Sep 2025).
• Limitations in handling complex web-based post-exploitation and nuanced legal clauses not easily partitioned into agentic modules (Mridul et al., 16 May 2025, Mai et al., 11 Sep 2025).
• Ambiguity in agent definition, complicating research reproducibility, system comparison, and policy standardization (Bent, 7 Aug 2025).

Future work includes ensemble methods, advanced deep learning integration (CNNs, RNNs, LSTMs), improved feature engineering, expanded benchmarking, and policy vocabulary standardization. Extending agentic frameworks to new modalities and adaptive, cross-domain tasks is an ongoing research trajectory.

7. Significance and Field Impact

TermiAgent systems, characterized by multidimensional agenticness, memory-activated architectures, domain-adapted terminology processing, and modular policy auditing, mark a milestone in the operationalization of agents in complex real-world settings. These frameworks address core requirements in research clarity, reproducibility, and practical applicability, spanning information extraction, security testing, legal auditing, and technology trend analysis. Their design—rooted in statistical, linguistic, and deep learning methods—provides a blueprint for next-generation agent-oriented AI systems capable of environmental impact, goal formulation, adaptive reasoning, and scalable deployment.