LLM-Agent-UMF: Unified Modeling Framework

• LLM-Agent-UMF is a unified framework that standardizes LLM-based agent cognition, planning, memory, and secure tool invocations.
• It employs modular pipeline patterns—including perception, memory, planning, and action—to enhance scalable multi-agent orchestration and reasoning.
• It enables applications in QA, requirements engineering, geosimulation, and multi-hop reasoning, ensuring clear, secure, and systematic agent interactions.

A LLM-Agent Unified Modeling Framework (LLM-Agent-UMF) is a comprehensive, principled architecture that formalizes the design, orchestration, and interoperation of LLM-based agents and their tool-augmented environments. LLM-Agent-UMF explicitly encapsulates agent cognition, planning, memory, profile configuration, tool invocation, and security within a modular and extensible architecture. Its application scope includes question answering (QA) over structured and unstructured knowledge, requirements engineering, geosimulation, and generalizable multi-step reasoning, providing a systematic foundation for the next generation of autonomous and collaborative AI systems (Hassouna et al., 17 Sep 2024, Zong et al., 22 Feb 2024, Padilla et al., 31 Jul 2025, Pham et al., 28 May 2025, Klimek, 10 Jun 2025, Hu et al., 5 Mar 2025).

1. Foundational Definition and Motivation

LLM-Agent-UMF defines an agent-based software architecture where LLMs, tools, and a novel “core-agent” interact in well-specified, orthogonal roles. The LLM is a pretrained neural LLM used as a black-box reasoner and planner; tools encompass APIs, databases, and external modules; the core-agent coordinates LLM calls, manages memory, configures agent profiles, orchestrates tool invocations, and enforces security. The motivation stems from the need to replace prior ad hoc LLM+tool chains with modularity, clear terminological distinctions, built-in security, and scalable multi-agent orchestration (Hassouna et al., 17 Sep 2024).

Formally, the unified agent is described as:

$A = (\mathit{LLM},\; \mathit{Tools},\; C)$

where $C$ is the core-agent defined as a tuple of five modules:

$$C = (M_{\mathrm{plan}}, M_{\mathrm{mem}}, M_{\mathrm{prof}}, M_{\mathrm{act}}, M_{\mathrm{sec}})$$

Here, $M_{\mathrm{plan}}$ handles planning, $M_{\mathrm{mem}}$ manages agent memory, $M_{\mathrm{prof}}$ configures the LLM profile/persona, $M_{\mathrm{act}}$ translates plans into actions (API or tool calls), and $M_{\mathrm{sec}}$ enforces security (Hassouna et al., 17 Sep 2024).

2. Modular Components and Pipeline Patterns

A minimal instance of LLM-Agent-UMF operationalizes agent cognition via four (or five) pipeline modules with explicit, LLM-mediated interfaces (Padilla et al., 31 Jul 2025):

• Perception: Encodes and decodes the environment or user input, often via LLM prompts.
• Memory: Aggregates, summarizes, and retrieves short- and long-term agent history.
• Planning: Translates beliefs, goals, and memory into sequences of executable actions or queries.
• Action: Executes environment updates, tool calls, or communication acts.
• Security (where present): Applies prompt/response filtering and confidentiality/integrity checks.

A prototypical pipeline:

$$\text{Env}(t) \rightarrow \text{Perception} \rightarrow \text{Memory} \rightarrow \text{Planning} \rightarrow \text{Action} \rightarrow \Delta\text{Env}(t)$$

LLM modules are invoked at each stage, leveraging prompt-dependent input representations and structured output parsing (Padilla et al., 31 Jul 2025). For structured workflows, a “core-agent” partitions reasoning from orchestration and strict module separation is enforced (Hassouna et al., 17 Sep 2024).

3. Role Taxonomy and Agent Decomposition

LLM-Agent-UMF architectures classify the core-agent along an authority axis:

• Active core-agent: Possesses all five modules ($$M_{\mathrm{plan}}, M_{\mathrm{mem}}, M_{\mathrm{prof}}, M_{\mathrm{act}}, M_{\mathrm{sec}}$$).
• Passive core-agent: Limited to $M_{\mathrm{act}}, M_{\mathrm{sec}}$.

This distinction enables composable multi-agent architectures:

Core-Agent Type	Modules Present	Typical Role
Active	Plan, Memory, Profile, Action, Security	Manager/Coordinator
Passive	Action, Security	Specialized tool operator

Active–passive compositions permit patterns such as one-active-many-passive (scalable orchestration) or fully active networks (consensus and fault tolerance), systematically balancing specialization, scalability, and security (Hassouna et al., 17 Sep 2024).

4. Orchestration Patterns and Generalization

A prominent protocol is the multi-role agent, as exemplified in the Triad KBQA framework (Zong et al., 22 Feb 2024):

• Generalist (G-Agent): Handles in-context learning and light transformation subtasks.
• Decision Maker (D-Agent): Ranks and filters candidates, orchestrates selection.
• Advisor (A-Agent): Generates final answers, manages retries, or falls back to LLM completions.

These roles decompose pipelines into four phases:

1. Question parsing (triplet extraction, template generation)
2. Entity/relation linking
3. Query construction (SPARQL/SQL, tool calls)
4. Final answer generation (retrieval or direct LLM output)

Formally, the orchestration is expressed as:

$$f(\text{Task})(Q, KB) = \bigoplus_{t=1}^T f(S_t)(Q, KB)$$

where subtasks $S_t$ are dispatched to agent roles with role-specific memory, prompts, and control logic (Zong et al., 22 Feb 2024).

UMF generalizes to tool-augmented or multi-step reasoning domains (e.g., Text2SQL, code synthesis) by replacing task-specific modules, memories, and prompts while preserving the multi-role or pipeline structure.

5. Formal Specification for Multi-Agent and Tool-Augmented Environments

In multi-agent geosimulation systems, LLM-Agent-UMF prescribes a four-module agent loop—perception, memory, planning, action—grounded in formal entities:

Environment:

$$\text{Env} = (\Theta_E, \mathcal{F}_E, \mathcal{L})$$

Agent:

$$a_i = (s_i, \mu_i, \phi_i, M_i, B_i, G_i, I_i, P_i, H_i, \text{LLM}_i)$$

Modules orchestrate via function compositions, parsing and summarizing input/outputs with light schemas, and synchronize via the environment or explicit message queues (Padilla et al., 31 Jul 2025). LLM calls are integrated in each module through standardized prompt-template and output parsers, with explicit context-window control.

6. Training, Verification, and Application Domains

Trainable LLM-agent frameworks (such as Agent-UniRAG (Pham et al., 28 May 2025)) optimize the entire planning–retrieval–generation pipeline via a single cross-entropy loss over synthetic, multi-turn traces, enabling end-to-end behavior tuning on domains such as single-hop and multi-hop retrieval-augmented generation (RAG). Synthetic data pipelines (e.g., SynAgent-RAG (Pham et al., 28 May 2025)) can bootstrap multi-stage, tool-using behaviors into relatively small open-source LLM backbones.

In requirements engineering, LLM-Agent-UMF underpins frameworks like LoRE+ (Klimek, 10 Jun 2025), coupling LLM-aided behavioral extraction, logical pattern mapping, and deduction-based model checking (PLTL/FOL), with automated code skeleton generation. This closes the loop from natural-language requirements to formally verified, executable models.

7. Evaluation, Best Practices, and Open Challenges

LLM-Agent-UMF design is evaluated with the Architecture Tradeoff and Risk Analysis Framework (ATRAF), emphasizing modularity, extensibility, security, performance, and maintainability (Hassouna et al., 17 Sep 2024). Comparative mappings reveal that state-of-the-art agents naturally instantiate active or passive core-agent variants, though often neglect explicit security. Recommended design defaults include separation of reasoning and orchestration, mandatory security modules, and the one-active-many-passive architecture for extensibility.

Open challenges include formal verification of agent optimality, scalable multi-agent synchronization, spontaneous (non-goal-directed) reasoning, human-like motivations, and efficient multimodal integration (Hu et al., 5 Mar 2025, Hassouna et al., 17 Sep 2024, Klimek, 10 Jun 2025).

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References:

• (Hassouna et al., 17 Sep 2024) LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Design of Multi Active/Passive Core-Agent Architectures
• (Zong et al., 22 Feb 2024) Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
• (Padilla et al., 31 Jul 2025) A survey of multi-agent geosimulation methodologies: from ABM to LLM
• (Pham et al., 28 May 2025) Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems
• (Klimek, 10 Jun 2025) RE-oriented Model Development with LLM Support and Deduction-based Verification
• (Hu et al., 5 Mar 2025) Unified Mind Model: Reimagining Autonomous Agents in the LLM Era