LLM-Based Agent

• LLM-based agent is an autonomous software entity that uses large language models to orchestrate structured, auditable workflows and multi-step reasoning.
• It integrates methodologies like game theory and sensitivity analysis to generate interpretable artifacts such as payoff matrices and role classifications.
• Its modular architecture supports swappable LLM modules and deterministic analyzers, enabling transparent evaluation and compliance in regulated domains.

A LLM-based agent is an autonomous software entity whose reasoning, planning, and action-selection processes are orchestrated by a LLM with large parameter count, such as GPT-4, GPT-5, or equivalent architectures. In an LLM-agent pipeline, the LLM does not function as a black-box response generator but instead as the central cognitive engine, controlling structured, multi-step workflows and coordinating tools, deterministic analyzers, and interfaces, often externalizing every intermediate reasoning artifact for auditable transparency. LLM-based agents support explicit task decomposition, structured state/action representations, and swappable module architectures—enabling rigorous, transparent, and inspectable AI reasoning across diverse application domains (Pehlke et al., 10 Nov 2025).

1. Core Principles and Architecture

The contemporary LLM-based agent departs from classical rule-based and policy-driven agents through modularized, pipeline architectures in which every logic or planning step is mediated or coordinated by an LLM. A canonical architecture consists of:

• Intake or Scenario Agent: Transforms an unstructured, human-provided scenario into structured metadata, defining system boundaries and stakeholders.
• Framework-specific Agents: Parallelized modules implementing distinct decision, analysis, or simulation frameworks (e.g., Vester Sensitivity, normal-form games, sequential games).
• Deterministic Analyzers: Post-process LLM-produced intermediates, classifying roles/equilibria, detecting cycles, or producing Pareto-optimal evaluations.
• Swappable LLM Modules: Every such module can be replaced, retempered, or substituted for specialization or ablation, supporting configurability and robustness.
• Artifact Summarization Agent: Aggregates all structured outputs into unified, human-readable explanations with reference to the entire reasoning chain.

All inter-agent or agent-analyzer communications are encoded in well-defined, structured JSON schemas, enabling tight integration, deterministic auditing, and external tool interoperability (Pehlke et al., 10 Nov 2025).

2. Decision-Support Methodologies Exemplified

The LLM-agent paradigm enables the integration of established structured analysis frameworks into a single, inspectable pipeline. Notable instantiations include:

• Vester’s Sensitivity Analysis: Variables generated per stakeholder, deduplication, $n \times n$ signed impact matrix construction, matrix-based role classification (active, critical, reactive, buffering) via mean-based thresholding of active/passive sums, feedback loop enumeration and heuristically scored ranking over system cycles.
• Normal-Form Game Analysis: LLM extraction of player and strategy sets, construction of payoff matrices, deterministic Nash equilibrium computation for pure (optionally, mixed) strategy profiles.
• Extensive-Form Game/Sequential Reasoning: Role-conditioned tree construction, node-wise action generation, leaf payoff assignment, and backward induction for subgame-perfect equilibria.

Each stage emits explicit, formal artifacts (e.g., variable cards, matrices, cycle lists, payoff tables, SPE paths), providing a granular, traceable workflow from initial scenario mapping to final interpretable recommendations (Pehlke et al., 10 Nov 2025).

3. Traceable, Auditable Reasoning Artifacts

A defining property of the LLM-agent workflow is the externalization of all reasoning steps. Instead of opaque, end-to-end LLM outputs, the agent system emits:

Artifact Type	Description	Example
Variable cards	Name, description, category, provenance for each factor	"Transport delay: external"
Signed impact matrix $I$	$n\times n$ LaTeX-style table, strengths and directions per variable pair	$I_{ij} = d_{ij}s_{ij}$
Role labels	Active, critical, reactive, buffering per variable	"Critical: port capacity"
Feedback loop list	Ranked explicit cycle compositions with summary scores	"Cycle: Delay→Cost→Delay"
Payoff matrix, NE	Strategies and equilibrium markers	$P_{ij}=(u_1,u_2)$
SPE path/profile	Tree nodes, strategies, payoffs, rationale	"Path: Delay→Invest"

These artifacts support inspection, reproducibility, and retrospective analysis, meeting the requirements for explainable AI and compliance in regulated domains (Pehlke et al., 10 Nov 2025).

4. Evaluation, Fidelity, and Human Alignment

LLM-based agents are empirically evaluated using quantitative, semantics-aligned metrics. In the illustrative logistics decision-support case, evaluation included:

• Semantic factor alignment: Embedding of LLM-extracted vs. human baseline factors, using named embeddings, with a match threshold ($\cos\ge 0.5$).
• Role agreement: Comparing LLM-assigned systemic roles with IVL human paper roles over matched factors.
• Rubric-based scoring: LLM judge with 8-point criteria, up to 100 points, covering aspects such as boundary clarity, feedback loop identification, actionability.

Key findings included mean factor alignment of 55.5% (all factors) and 62.9% (core subset), role agreement ≈57%, and LLM rubric scores on par with human baselines (LLM mean 92.97/100 vs. human 93/100), demonstrating structural and qualitative equivalence in a fully auditable process (Pehlke et al., 10 Nov 2025).

5. Module Swapping, Extensibility, and Open Challenges

A distinctive feature is the agent’s modular, swappable architecture:

• LLM Models: Configurable at each pipeline step (e.g., GPT-4 vs. GPT-5), tunable temperature and reasoning effort, with potential for task-specialized/fine-tuned agents.
• Deterministic Solvers: Equilibria and classification analyzers operate as standalone, replaceable code modules, decoupling stochastic LLM outputs from pivotal system decisions.
• Interface Consistency: All inter-module outputs standardized via structured schemas for maximal interoperability.

Current limitations include the lack of an orchestration layer (manual workflow routing), sensitivity of role classifications to small matrix perturbations, and reliance on heuristic feedback loop scoring. Open research questions involve automating orchestration, ensemble or self-consistency correction for systemic variability, diversified model selection per subtask, and the incorporation of global cybernetic feedback/evaluation phases (Pehlke et al., 10 Nov 2025).

6. Significance within the Research Landscape

The LLM-based agent approach operationalizes classical system analysis, decision theory, and multi-agent reasoning frameworks within a single, configurable and fully-auditable architecture. It bridges opaque deep learning pipelines and classical, human-auditable expert analysis by making every intermediate decision explicit, structured, and open to review. Such agents are well-positioned for regulated domains—such as logistics, environmental analysis, and strategic planning—where both transparency and flexibility are essential.

The full externalization of reasoning, structured artifact chaining, task-specific module swapping, and human-aligned evaluation metrics differentiate modern LLM-based agents from earlier end-to-end or black-box LLM deployments, providing a foundation for explainable, robust, and trustworthy AI in decision support and beyond (Pehlke et al., 10 Nov 2025).