LLM-Empowered CAE Agent Architecture

• The topic defines LLM-Empowered CAE Agents as modular systems that leverage LLMs for high-level reasoning and planning to orchestrate complex engineering workflows.
• Its architecture integrates decoupled capabilities such as reception, planning, methodology, profile, and tool integration to ensure scalability and rapid domain adaptation.
• The framework employs dynamic tool discovery and hierarchical task decomposition, reducing reliance on monolithic pipelines and mitigating LLM hallucinations.

An LLM-Empowered CAE Agent is a computationally autonomous engineered system in which a LLM serves as a reasoning, planning, and orchestration core for engineering workflows. Rather than relying on static scripts or monolithic automation pipelines, these agents employ modular architectures—often with collaborative or hierarchical multi-capability organization—to execute, adapt, and extend complex engineering processes (e.g., design, simulation, analysis, and verification). Architectures draw on integrated modules for planning, memory, tool invocation, and dynamic user context management, frequently incorporating service computing paradigms to maximize extensibility and real-world applicability.

1. Architectural Foundations and Collaborative Capability Model

LLM-Empowered CAE Agents are structured according to a set of decoupled, cooperative modules ("capabilities") that reflect service-computing design principles (Xu et al., 22 Mar 2024). The central architectural components are:

• Reception Capability: Handles user-facing input, transforming unstructured requests into structured task descriptors.
• Workflow Capability: Instantiates and orchestrates a workflow for each request, managing execution logic by interfacing with planning, tool integration, and profile modules.
• Planning Capability: Typically LLM-backed, decomposes tasks into sequential, executable sub-tasks, optionally integrating external facts/methods from a Methodology Capability.
• Methodology Capability: Injects domain-specific or expert-guided process knowledge to ground the planning output and reduce LLM hallucinations.
• Profile Capability: Maintains long-term memory and user/system context, supporting personalization and continuity; workflow instances supply short-term context.
• Tool Integration Capabilities: Leverage a “Registration-Discovery-Invocation” framework. Tools are indexed and discovered by matching task requirements via LLM inference, and Tool Brokers facilitate fast integration of new services.

System extensibility is achieved by partitioning task handling across these modules, permitting incremental upgrades (e.g., swapping out LLMs, adding new tools, updating domain knowledge bases) without recoding the entire agent.

2. Planning, Reasoning, and Execution Formalism

Task decomposition and execution in LLM-Empowered CAE Agents is mathematically expressed using:

$$\text{PlanCap}(Task, Methodology) \rightarrow [\text{Proc}(ST_1), \ldots, \text{Proc}(ST_n)]$$

Each $\text{Proc}(ST_i)$ (processing procedure for sub-task $i$) is characterized by:

• $\text{Execute}(ST_i)$: Run the sub-task
• $\text{Branch}(ST_i)$: Conditional logic (decision points)
• $\text{Loop}(ST_i)$: Iterative or repeated execution structure

Tool discovery for sub-task execution is expressed as:

$$\{T_{selected}, T_{param}[input]\} = \text{DiscoverTool}(U_{req}, \{T_{registered}\})$$

where the agent matches user requirements $U_{req}$ to system-registered tools $\{T_{registered}\}$ via LLM-driven inference.

Critically, planning capabilities are enriched by integrating domain-specific process guidelines, mitigating typical LLM shortcomings like hallucination and non-robust context extension.

3. Modularization, Extensibility, and Distributed Reasoning

Unlike monolithic LLM agent paradigms, the CACA Agent (Xu et al., 22 Mar 2024) and frameworks like LLM-Agent-UMF (Hassouna et al., 17 Sep 2024) advocate a distributed, modular structure:

• Capabilities are developed as semi-independent microservices.
• LLM responsibility is limited to high-level reasoning, planning, and inferential tool matching, with factual/methodological knowledge injected by external modules.
• Computing responsibilities can be allocated to the most appropriate service (e.g., transitioning from high-cost, external LLM endpoints to domain-specialized, locally deployed models).
• New application scenarios (e.g., weather-dependent travel, integration of a new solver or CAD tool in engineering) become extensible by simple registration and methodology updates—not by LLM retraining.

This approach strongly enhances robustness, rapid domain adaptation, and scalability for industrial applications.

4. Tool Orchestration and Real-World Integration

Tool invocation is managed via a service computing pattern, separating tool registration/discovery from execution. The workflow capability queries the tool registry to match tasks with appropriate services, invoking them with parameter sets inferred by LLM reasoning.

• New tools/services are integrated rapidly via broker-mediated registration and can be discovered and invoked by workflow logic with no architectural overhaul.
• Extensibility is illustrated by scenario demos: e.g., a travel recommendation agent integrates weather APIs on demand—first by updating planning logic, then by registering a weather query tool, enabling agents to exclude adverse-weather destinations in their recommendations.
• This orchestration design is directly applicable to CAE: simulation, CAD processing, database mining, and result post-processing each map to discrete, discoverable tool services.

5. Case Study: Demo Scenarios and Extensibility

Demo scenarios presented in (Xu et al., 22 Mar 2024) highlight the extensibility of LLM-Empowered CAE Agents:

Scenario 1: Standard travel workflow

• User query triggers workflow instantiation, planning decomposition, user profile lookup, and sequential tool invocation to deliver a composite itinerary.

Scenario 2: Real-time planning expansion

• Planning process is dynamically extended via the methodology capability (e.g., adding weather-based filtering with a new sub-task and corresponding tool).

Scenario 3: Seamless tool extension

• On-demand registration of a new external service (e.g., weather information API) enables immediate expansion of agent function—demonstrated by subsequent queries accurately referencing new environmental constraints.

This pattern generalizes to CAE where new simulation modules, data analysis routines, or domain-specific checks can be integrated with minimal system interruption.

6. Implications for Deployment and Evolution

LLM-Empowered CAE Agents present several implications for engineering system development:

• Scalability and Upgradeability: Modular service-oriented architecture supports the evolution of agent capabilities independent of LLM retraining cycles or monolithic platform upgrades.
• Reduction of Single-LLM Dependency: By distributing reasoning and factual knowledge, risks associated with LLM limitations (cost, domain fit, output robustness) are minimized.
• Rapid Domain Adaptation: Injection of methodology knowledge and registration of new services enable instant deployment of new operational logic, aligning with agile engineering workflows.
• Enhanced Reliability: Two-tier planning (LLM plus external methodology) reduces hallucination, supporting higher-fidelity automation even in mission- or safety-critical engineering contexts.

Implementation patterns suggested by CACA Agent architectures—distributed collaborative capability modules, dynamic discovery/invocation tool orchestration, profile memory management—form the technological foundation for next-generation, robust, and adaptive CAE automation agents.

7. Summary Table: Core Capabilities and Their Roles

Capability Type	Function Summary	Key Formula / Interface
Reception	User interface, input parsing	N/A
Workflow	Orchestrates execution, manages flow logic	Workflow(Task) → Plan + Tool Invocation
Planning (LLM)	Decomposes tasks into procedural steps	PlanCap(Task, Methodology) → Procedures
Methodology	Inject external knowledge, process rules	Factual/Method Input to Planning
Profile (Memory)	User-specific/system configuration and history	Short-term: Workflow instance; Long-term: Profile
Tool Capability/Broker	Tool registry, service discovery/invocation	DiscoverTool(U_{req}, {T_{reg}}) → T_{sel}

This typology reflects the blueprint for engineering robust, extensible agents capable of orchestrating and adapting complex domain workflows while leveraging LLMs for reasoning and planning within a service-computing ecosystem.