# Teaming System Engineers, AI Agents, and tools for Model-Based System Engineering

Ramon Conejo, Eric Jenn, Jean-Marie Gauthier, Emilie Perreau

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Ramon Conejo, Eric Jenn, Jean-Marie Gauthier, Emilie Perreau. Teaming System Engineers, AI Agents, and tools for Model-Based System Engineering. 13th European Congress of Embedded Real Time Systems (ERTS), Feb 2026, Toulouse, France. 10.82331/ERTS.2026.45. hal-05513849

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# Teaming System Engineers, AI Agents, and tools for Model-Based System Engineering

Ramon Conejo-Laguna¹, Jean-Marie Gauthier², Eric Jenn³ and Emilie Perreau⁴

1,2,3 IRT Saint-Exupery, Toulouse, France
firstname.lastname@irt-saintexupery.com

⁴ Univ. Grenoble Alpes, CNRS, Grenoble INP, G-SCOP
emilie.perreau@grenoble-inp.fr

## ABSTRACT

Giving computers the capability to “understand” natural language was a prerequisite to make them real contributors to system engineering activities. Indeed, during the early engineering phases, concepts remain fuzzy, polysemic, and requirements are generally informal. Today, thanks to Large Language Models, this condition seems to be fulfilled and we can envisage teams of collaborating humans and AI agents. In this paper, we present our workflow and its supporting tools towards that goal. We describe and illustrate our approach on the functional analysis activities of a small robot.

## 1. INTRODUCTION

Systems engineering faces inherent challenges that have persisted throughout its evolution as a discipline. The complexity and interdisciplinary nature of modern systems create significant barriers to collaborate and integrate all the required knowledge. During early design phases, engineers must navigate incomplete information, ambiguous requirements, and evolving system boundaries while coordinating across multiple domains of expertise [1].

Recent breakthroughs in AI, especially in systems that understand human language, now allow engineers to work with AI helpers instead of just using them as basic tools [2]. These "smart assistants" can handle tricky logic problems, work with both free-form text and organized data, and help brainstorm design options. This means engineers and AI can team up more effectively [3].

In this paper, we explore the opportunities and challenges of such interaction through the concrete and realistic use case of an escort robot. We focus our analysis of the functional decomposition engineering task. This study includes the decision-making process, interaction with modeling tools, and human-AI communication dynamics. The findings are validated through the development of a Model-Based Systems Engineering (MBSE) tool interface.

The rest of the paper is organized as follows: Section 2 defines the problem at stake and presents related work; Section 3 gives an overview of our workflow and its implementation; Section 4 present our results; Section 5 concludes the document.

## 2. PROBLEM STATEMENT AND RELATED WORK

MBSE is applicable to most system engineering activities, as soon as a model is available. This includes system requirements, design, analysis, verification, and validation activities, that covers most the system life cycle phases. However, in this paper, we concentrate our effort on the activity of *functional analysis* and, more specifically on *functional refinement* (aimed at building a functional tree), *functional chains composition*, and *function allocation* to system components.

### 2.1. Issues and opportunities

Functional decomposition and allocation involves many different sub-activities and raises many challenges. In this work, we limit our analysis to the challenges listed hereafter.

**Defining the objective activity.** The first and most important element is to have a clear definition of the expected outputs of the activity. Strangely enough, the actual objective of the “functional decomposition” neither very clear nor consensual because, as reminded in [4], the concept of function is itself non-consensual. However, in order to share part of this activity or collaborate with an AI agent, this activity must be defined precisely.

**Exploring the design space.** There is usually not only one possible way to refine a function. Choosing one solution over another is sometimes completely subjective and arbitrary, but it is usually a matter of optimizing one or several criteria, each criterion being relative to some specific point of view. Making an objective design decision rely on the capability to evaluate several candidate solutions, and chose the best one.

**Expressing the cost function and constraints.** The design process is actually an optimization problem defined by a multi-criteria cost function and a set of constraints. Often, a design choice is justified *a posteriori* (possibly in a dedicated document such as a *Design Justification Document*), but the optimization problem is not clearly expressed. Automating the design process requires those elements to be explicitly defined. In addition, the cost function usually involves criteria in different forms (quantitative, qualitative, etc.) that are difficult to integrate in a single formula. A design solution is generally a trade-off between all the criteria.

**Managing multiplicity of viewpoints and combining expertise.** In practice, the set of criteria and constraints relate to specific engineering domains such as performance engineering, dependability engineering, hardware or software

<footnote>Ramon Conejo-Laguna et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</footnote>
engineering, etc. Refining the design involve expertise in all
domains. Usually, expertise is distributed among departments
and engineers, so the design process is performed sequentially,
one solution being designed according to a subset of the
viewpoints, then reviewed and modified according to another,
and back and forth up to the point some acceptable "fixed
point" is reached.

**Stopping the activity.** Building a functional tree is in principle a top-down refinement process in which each function is decomposed into a set of sub-functions, repeatedly, down to the point where the function is sufficiently simple not to require any further decomposition or is directly provided by some available component. In practice, the process mixes top-down and bottom-up approaches.

**Exploiting existing assets.** The "bottom-up" approach relies on existing assets. Those assets may be existing COTS or in-house components, or existing modeling artefacts developed for other systems. Exploiting this information usually relies on human expertise and experience, documentation, and interaction between engineers.

**2.2. Related works**

**Functional analysis.** Defining a "good" functional decomposition is complex. Different conceptualisations of function exist in engineering design research, with meanings varying and sometimes coexisting within a single line of research [5]. The ambiguity surrounding function modelling and its execution presents a notable barrier to its use in industry. Instead of focusing solely on theoretical definitions, engineers in industry often prioritise pragmatic methods for building useful models [4]. What constitutes a "good" decomposition often depends on its usefulness and applicability for specific reasoning applications [6].

Evaluating the effectiveness of functional decomposition, particularly concerning problem space exploration during early-stage design, is a key area of study. [7] proposes several metrics for this evaluation: Quantity of unique functions (M1), which counts distinct functions identified; Breadth and depth of the hierarchical structure (M2), which assesses the spatial geometry of the decomposition tree at different levels; and Relative semantic coverage ratio (M3), which, against a benchmark model, quantifies how comprehensively the model captures the range of meanings within the problem space and covers the semantic breadth of user needs. Beyond these problem space exploration metrics, there are other characteristics contribute to an effective functional decomposition [5]. These include the interpretability of the model, referring to how consistent and precise its interpretation is across individuals, domains, and expertise levels. Some approaches, like the System State Flow Diagram (SSFD), aim for high consistency and precision by defining functions in relation to state transitions and measurable attributes [8]. The ability to maintain physics principles and support physics-based reasoning is also noted. For complex multidisciplinary systems, an effective model might be one that can effectively bridge different disciplines and represent various flows like material, energy, and information. Ultimately, a "good" functional decomposition is one that demonstrates utility and applicability for its specific context and purpose, supporting necessary reasoning activities [6].

**LLM for system engineering.** Several initiatives, both academic and industrial, have demonstrated the potential of LLMs integrated into MBSE workflows [9] [10] [11]. Tools like MITRE's SELMA and MBSEmaestro illustrate how AI assistants can automate model creation while ensuring a certain level of consistency and compliance. Research efforts also focus on model validation, cost function articulation, and requirement verification through advanced LLM techniques [12]. These developments suggest a shift from static tool usage to dynamic AI collaboration, where LLMs act as co-designers. Yet their effective deployment in high-stakes, domain-specific contexts like RE still demands overcoming significant challenges related to reliability, traceability, and domain adaptation [13]. Our approach builds on this trend, emphasizing task framing, domain adaptation, and workflow integration to explore how AI agents can contribute meaningfully to functional engineering activities.

**3. WORKFLOW, TECHNOLOGICAL BRICKS AND IMPLEMENTATION**

**3.1. Workflow**

Our goal is not merely to integrate LLMs into MBSE
workflows, but also to revisit and formalize the functional
analysis activity itself through this AI-human interaction. The
core idea is to position the AI as an active design partner,
contributing to task resolution rather than simply executing
predefined instructions.

The workflow rests on three foundations: (i) task framing, which defines the engineering activity through expected outputs and interaction patterns; (ii) AI orchestration, where LLM agents follow tailored prompting strategies enhanced via RAG pipelines; and (iii) evaluation and iteration, combining automated and human-in-the-loop refinement of outputs.

**3.2. Technological bricks**

**LLMs.** Our study is executed with the latest OpenAI models as of the time of writing this paper: gpt-5, gpt-5-mini, gpt-4.1, gpt-4.1-mini. Even though the solution can be adapted for other LLM suppliers, our preference on OpenAI is supported by its dominance in their reasoning and other cognitive scenarios [14].

**Agentic architectures and MCP.** The implementation also relies on two other important technological bricks: (i) AI agents organized as a agentic architecture and (ii) the Model Context Protocol to support agent and tool interactions.

**3.3. Implementation**

This section describes the implementation of the workflow
presented in Section 3.

**3.3.1. Agentic architecture**

The design of our agentic architecture draws on core insights
from recent work on AI agent systems, particularly the
patterns identified in [15]. In line with these emerging
practices, we adopt a role-specialized and cooperating agents
system rather than relying on a monolithic assistant. Each
agent is aligned with a specific stage in the functional analysis
 workflow.
**Pre-Decomposition Agents.** These agents ensure that all conditions are met for a meaningful and justifiable decomposition of the root function. They are focused on interpreting, verifying, and contextualizing the functional requirement. Among them there are: (i) Specification Understanding Agent that checks for proper understanding of terms, domain-specific lexicon usage, and the ability to generate representative scenarios, (ii) the Requirement Quality Agent that verifies qualitative measures such as clarity and testability and (iii) the Context Readiness Agent that detects missing prerequisite information, and validates the existence of high-level architectural context.

**Core Decomposition Agents.** Once the preconditions are met, the decomposition agents carry out the core activity of decomposing the root function: (i) the Functional Decomposer Agent: proposes a set of meaningful subfunctions based on each parent function with a flow-based vision for the new subfunctions (ii) the Requirement Refiner & Tracer Agent initiates the decomposition process by refining the high-level functional requirement into a more structured and traceable set of sub-requirements, (iii) the Constraint Allocator Agent: Assigns non-functional constraints (e.g., performance, robustness, safety) to subfunctions.

**Coordination and Strategy.** The overall process is orchestrated by a Planner Agent, which maintains a global memory of the user conversation and handles sequencing and delegation among agents. It also interfaces with the MBSE tool via the MCP protocol.

<figure><img src="image_2.png" /><figcaption>**Table 1. Simplified prompt extract of the instruction set provided to the OpenAI agent**</figcaption></figure>

### 3.3.2. RAG architecture

To enhance the contextual understanding and domain specificity of our AI agents, we integrated a Retrieval-Augmented Generation (RAG) architecture into our MBSE workflow. This architecture allows agents to access and utilize external knowledge sources (e.g., component datasheets), ensuring that their outputs are grounded in up-to-date and relevant information.

### 3.3.3. Prompt-engineering

In this section, we describe how we devices the LLM agents' prompts from the informal description of the FA activity. To construct effective prompts, we follow several established principles [16][17][18] such as: inducing chain-of-thought reasoning, in-context learning and task decomposition. Along with those techniques, the prompt is enhanced with the RAG technique mentioned previously plus the inherent reasoning capabilities present in the OpenAI reasoning models.

### 3.3.4. Integration with the MBSE environment

The developed MBSE web-based interface allows the user to edit manually a functional tree while interacting with an LLM agent using a chat panel. Messages are processed in the backed, where the planner agent coordinates the team of agents in order to answer the user request. Two possible outcomes are expected. First a feedback message from the LLM agent with a final answer. Second, an optional set of editions made on the tree view by the agents team via MCP calls. The web application follows the same design structure as the EasyMOD tool [19]. The motivation behind is to integrate the AI-functional decomposition assisted capability to this tool, which allows at the same time a direct integration with matured MBSE frameworks such as Capella or Cameo.

<figure><img src="image_3.png" /><figcaption>**Figure 1. View of the extended MBSE tool**</figcaption></figure>

## 4. USE CASE AND EVALUATION

### 4.1. Use Case

For this study, we use the BIRD project as a practical use case to evaluate the proposed tool's effectiveness. BIRD represents a research initiative led by the IRT Saint Exupery with dual objectives: (i) develop a robotic system capable of autonomously escorting visitors from the reception area of the B612 building to designated meeting rooms., (ii) integrate and showcase various technological components developed across multiple departments.
The experiment was structured around the progressive refinement of multiple functional requirements extracted from the Escort Visitor specification. Each refinement scenario corresponded to a distinct subfunction or system behavior and was handled interactively by the human engineer with support from the agentic system. As an example, the requirement illustrated below was treated given the constrains the physical distance between the escorting robot and the visitor during the mission.

**Title:** Max distance between the robot and the visitor

**Requirement:** At any time during the displacement, and as long as the visitor's acceleration remains less or equal to 1ms⁻², the distance between the robot and the visitor shall be in [0.5m+0.1xs,1m+0.1xs][0.5m+0.1xs,1m+0.1xs] where s is the absolute speed of the robot.

Each scenario followed a structured five-step process: (1) task framing by the engineer or pre-decomposition agents; (2) functional decomposition by the core agent team, proposing coherent breakdowns; (3) constraint propagation and traceability to maintain technical and safety requirements; (4) allocation to existing components or identification of new capabilities; and (5) final review and refinement by the human engineer.

## 4.2. Analysis and Assessment

The evaluation of our multi-agent MBSE workflow is centered on two axes: (i) the nature of the Human-AI teaming, and (ii) the engineering quality of the resulting functional decompositions. Rather than limiting the evaluation to execution speed or automation ratio, we focus on how the tool assisted engineers in managing complexity, structuring knowledge, and improving traceability.

### 4.2.1. Human-AI teaming

The system aims to enhance the engineer's reasoning process without imposing decisions. The agents proposed structured decompositions, grounded in the requirement base and operational context. They are able to justify their suggestions and react to feedback with rephrased or alternative proposals.

The evaluation approach for this interaction is grounded in a theory-based definition of interaction heuristics, based on core concepts from Human-AI Teaming (HAT) literature. The heuristics refines in alignment with key domain criteria such as shared situational awareness, role allocation [20] and mutual directability [21].

### 4.2.2. Quality of functional decomposition

Functional decomposition quality are addressed using qualitative criteria, including: validity—each function supported its parent's objective and preserved the top-level behavior; and allocability—all leaf nodes were clearly assigned to existing components, COTS modules, or planned extensions, with no ambiguous mappings.

**Practical Example: Allocability Assessment**

**Function:** "Orient the camera to keep the visitor in the picture"

**Allocability Evaluation Checklist:**

- **Component Assignment Clarity** (Score: 0-3)

- 0: No component assignment identified

- 1: Multiple possible components, unclear which should be assigned

- 2: Primary component identified with minor secondary assignments

- 3: Unambiguous assignment to specific component(s)

**Example Assessment:** This function scores 3 as it is clearly assigned to the **Pan-Tilt Camera Module** (hardware component) working in conjunction with the **Vision Processing Unit** (existing COTS module).

- **Component Availability** (Score: 0-3)

- ...

**Overall Allocability Score:** 10/12 (83%) - indicating high allocability with clear component assignments and only minor coordination ambiguities to resolve.

## 5. CONCLUSION AND FUTURE WORK

This paper shows the feasibility and potential of integrating AI agents into Model-Based Systems Engineering workflows, specifically for functional analysis activities. Through our multi-agent architecture applied to the BIRD escort robot case study, we have shown that AI can serve as an active design partner rather than merely an execution tool. Several promising research directions emerge from this work, including enhanced domain adaptation through sophisticated RAG architectures and multi-criteria optimization for handling complex design trade-offs. Future research should also address collaborative workflows supporting multiple engineers working simultaneously with AI agents, real-time learning capabilities that improve recommendations over time, and the development of appropriate governance frameworks for responsible adoption.

While our results are encouraging, broader validation across different domains and system complexities is needed.
REFERENCES

[1] INCOSE. 2015. "A Complexity Primer for Systems Engineers." https://www.incose.org/docs/default-source/ProductsPublications/a-complexity-primer-for-systems-engineers.pdf.

[2] Mushtaq, Abdullah, Muhammad Rafay Naeem, Ibrahim Ghaznavi, Muhammad Imran Taj, Imran Hashmi, and Junaid Qadir. 2025. "Harnessing Multi-Agent LLMs for Complex Engineering Problem-Solving: A Framework for Senior Design Projects." ArXiv.Org. January 2, 2025. https://arxiv.org/abs/2501.01205v1.

[3] Tran, Khanh-Tung, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O'Sullivan, and Hoang D. Nguyen. 2025. "Multi-Agent Collaboration Mechanisms: A Survey of LLMs." ArXiv.Org. January 10, 2025. https://arxiv.org/abs/2501.06322v1.

[4] C. Eckert, 'That which is not form: The practical challenges in using functional concepts in design', AIEDAM, vol. 27, no. 3, pp. 217–231, Aug. 2013, doi: 10.1017/S089006041300022X.

[5] M. Bohm, C. Eckert, C. Sen, V. Srinivasan, J. D. Summers, and P. Vermaas, 'Thoughts on benchmarking of function modeling: Why and how', AIEDAM, vol. 31, no. 4, pp. 393–400, Nov. 2017, doi: 10.1017/S0890060417000531.

[6] J. D. Summers, C. Eckert, and A. K. Goel, 'Function in engineering: Benchmarking representations and models', AIEDAM, vol. 31, no. 4, pp. 401–412, Nov. 2017, doi: 10.1017/S0890060417000476.

[7] J. She, E. Belanger, and C. Bartels, 'Evaluating the effectiveness of functional decomposition in early-stage design: development and application of problem space exploration metrics', Res Eng Design, vol. 35, no. 3, pp. 311–327, Jul. 2024, doi: 10.1007/s00163-024-00434-w.

[8] U. Yildirim, F. Campean, and H. Williams, 'Function modeling using the system state flow diagram', AIEDAM, vol. 31, no. 4, pp. 413–435, Nov. 2017, doi: 10.1017/S0890060417000294.

[9] "AI Integrated as an MBSE Modeling Assistant in SysON - The Official Voice of the Obeo Experts." n.d. Accessed June 9, 2025. https://blog.obeosoft.com/ai-integrated-as-an-mbse-modeling-assistant-in-syson.

[10] "MITRE's Systems Engineering Language Modeling Assistant (SELMA) | MITRE." 2024. October 9, 2024. https://www.mitre.org/our-impact/intellectual-property/mitres-systems-engineering-language-modeling-assistant-selma.

[11] "MBSEmaestro.AI™: MBSE + AI Tools & Services." n.d. MBSEmaestro™. Accessed June 9, 2025. https://MBSEmaestro.AI/index.html.

[12] Johns, Brian, Kristina Carroll, Casey Medina, Rae Lewark, and James Walliser. 2024. "AI Systems Modeling Enhancer (AI-SME): Initial Investigations into a ChatGPT-enabled MBSE Modeling Assistant." INCOSE International Symposium 34 (1): 1149–68. https://doi.org/10.1002/iis2.13201.

[13] Cheng, Haowei, Jati H. Husen, Sien Reeve Peralta, Bowen Jiang, Nobukazu Yoshioka, Naoyasu Ubayashi, and Hironori Washizaki. 2024. "Generative AI for Requirements Engineering: A Systematic Literature

Review." ArXiv.Org. September 10, 2024. https://arxiv.org/abs/2409.06741v1.

[14] "Capabilities - Holistic Evaluation of Language Models (HELM)." n.d. Accessed June 9, 2025. https://crfm.stanford.edu/helm/capabilities/latest/.

[15] T. Masterman, S. Besen, M. Sawtell, and A. Chao, 'The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey', Apr. 17, 2024, arXiv: arXiv:2404.11584. doi: 10.48550/arXiv:2404.11584.

[16] S. Schulhoff et al., 'The Prompt Report: A Systematic Survey of Prompting Techniques', Dec. 30, 2024, arXiv: arXiv:2406.06608. doi: 10.48550/arXiv:2406.06608.

[17] S. Vatsal and H. Dubey, 'A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks', Jul. 24, 2024, arXiv: arXiv:2407.12994. doi: 10.48550/arXiv:2407.12994.

[18] P. Sahoo, A. K. Singh, S. Saha, V. Jain, S. Mondal, and A. Chadha, 'A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications', Feb. 05, 2024, arXiv: arXiv:2402.07927. doi: 10.48550/arXiv:2402.07927.

[19] "EasyMOD: Revolutionizing Systems Engineering with Intelligent and Collaborative Interfaces - IRT Saint Exupéry • Technological Research Institute." December 2, 2024. https://www.irt-saintexupery.com/project-easymod/, https://www.irt-saintexupery.com/project-easymod/.

[20] Endsley, M. R. 2023. "Supporting Human-AI Teams: Transparency, Explainability, and Situation Awareness." Computers in Human Behavior 140 (March):107574. https://doi.org/10.1016/j.chb.2022.107574.

[21] Sycara, K., and Gita S. 2006. "Literature Review of Teamwork Models."