Hybrid Agentic AI for Siemens NX

Building an AI Co-Engineer That Executes, Verifies, and Assists Inside Siemens NX

To protect client confidentiality, all customer-identifying details have been omitted.

Executive Summary

A confidential engineering client wanted to go beyond static automation and conventional CAD scripting. Their goal was to create a new kind of engineering assistant inside Siemens NX: an AI system that could understand user intent, decide which NX tools to use, automatically populate the required parameters, execute actions inside the CAD environment, verify the results, and support engineers during design and construction tasks.

We developed a hybrid agentic AI solution for Siemens NX that combines natural-language reasoning with deterministic tool execution inside NX. Instead of behaving like a chatbot that only explains commands, the system acts as an operational co-engineer. It can interpret requests, select the right tool chain, fill in parameters based on context, perform actions in NX, check whether the result is valid, and continue the workflow until the requested task is completed or clarification is needed.

The result is a practical AI assistant that helps engineers work faster, reduce repetitive manual work, improve consistency, and receive direct support during modeling and construction processes.

The Challenge

The client’s engineering teams were working in an environment where complex design tasks often required multiple manual steps, repeated validation, and detailed knowledge of internal NX workflows. Even experienced users lost time switching between thinking, modeling, checking, correcting, and re-running actions.

Traditional automation helped only to a point. Rule-based scripts could handle fixed workflows, but they could not adapt well when a user’s request was ambiguous, incomplete, or dependent on the current state of the model. On the other hand, a pure language model could describe what should be done, but it could not reliably execute operations inside Siemens NX with the precision and control required in production engineering.

The client needed a system that could bridge both worlds: flexible AI reasoning and reliable CAD execution.

Project Goals

The project was designed around five clear goals.

First, the AI had to understand engineering intent expressed in natural language.

Second, it had to execute real actions inside Siemens NX rather than only generate suggestions.

Third, it had to choose the correct tools autonomously and fill in tool parameters based on model context, user instructions, and workflow history.

Fourth, it had to validate results after execution so that operations were not treated as complete unless they actually succeeded.

Fifth, it had to support engineers during construction work by acting as a guided assistant, not as an uncontrolled black box.

Our Solution

We built a hybrid agentic AI architecture for Siemens NX that combines a reasoning layer with a controlled tool-execution layer inside the NX environment.

At the center of the system is an agent orchestration logic that interprets the user’s goal and breaks it into executable steps. These agents do not stop at planning. They actively decide which NX tools must be called, assemble the required parameters, trigger the operation, inspect the outcome, and either continue, correct, or ask the user for clarification.

This created a practical loop of:

understand → plan → execute → verify → assist

rather than a one-shot prompt-response interaction.

The solution was “hybrid” in the truest sense. The AI handled interpretation, sequencing, and context-aware decision-making, while deterministic NX tool calls handled execution, measurements, feature operations, and validation. This made the system far more robust than either a pure script library or a pure conversational assistant.

What the AI Can Do Inside NX

The delivered solution was designed to support real engineering work, including:

• interpreting user instructions in natural language

• selecting appropriate NX operations for the requested task

• auto-filling parameters for tool calls based on part context and user intent

• executing commands inside Siemens NX

• checking geometry state, model state, or operation results after execution

• helping during construction and feature creation workflows

• guiding the user when missing information or conflicting conditions are detected

• reducing repetitive modeling interactions through reusable AI-driven workflows

This means the AI is not limited to “explaining how NX works.” It can actively participate in work execution inside NX.

How the Workflow Works

1. User Request

An engineer gives an instruction in natural language, such as creating, modifying, checking, or assisting with a construction task in NX.

2. Intent Interpretation

The AI analyzes the request, the current workflow state, and the active engineering context. It identifies the likely objective, required sub-steps, and the tools that may be needed.

3. Tool Selection

The responsible agent selects the appropriate NX-capable tool or tool sequence. The system is not restricted to a single action; it can assemble a multi-step plan when needed.

4. Automatic Parameter Filling

Instead of relying on manually entered command parameters, the agents derive the inputs themselves from available context. This can include model state, selections, geometry references, operation history, constraints, or previously gathered tool output.

5. Execution in Siemens NX

The selected action is executed inside the NX environment through the tool layer.

6. Verification and Correction

After execution, the AI checks whether the intended result was actually achieved. If not, it can adjust the next step, retry with corrected logic, or request clarification from the user.

7. Engineering Assistance

During construction workflows, the system can act as a co-pilot by helping with decisions, suggesting next steps, and reducing the need for repeated manual interaction.

Technical Approach

The technical approach was aligned with Siemens’ automation model for NX, where NX Open and journaling provide the foundation for extending and automating NX behavior.

Our implementation layered agentic orchestration on top of a controlled NX tool interface.

The reasoning layer was responsible for intent detection, task decomposition, context handling, tool selection, and decision-making. The execution layer exposed structured tool calls into Siemens NX. Each tool had a clearly defined purpose, expected inputs, and verifiable outputs. Instead of hardcoding every parameter path, the agents dynamically filled parameter values based on the current engineering situation.

A validation layer then checked operation outcomes before the workflow could move forward. This was essential. In engineering environments, “command sent” is not the same as “task completed.” The system had to inspect results, detect mismatches, and react accordingly.

This architecture enabled a controlled form of autonomy: flexible enough to be useful, but deterministic enough to be trusted.

Why a Hybrid Agentic Approach Was the Right Choice

A pure rule engine would have been too rigid.

A pure conversational AI would have been too unreliable for direct CAD execution.

The hybrid approach combined the strengths of both.

The agentic layer made the system adaptable. It could interpret broader goals, work across multiple steps, and react to changing design context. The deterministic tool layer made the system dependable. Every important action was executed through defined NX-capable functions, not through vague text generation.

That combination is what made the system practical for real Siemens NX workflows.

Business Value

For the client, the value was not only technical. It was operational.

The system reduced repetitive engineering effort by turning multi-step NX interactions into assisted workflows. It improved consistency because similar tasks were handled through the same validated logic instead of individual user habits. It accelerated execution because engineers no longer had to manually navigate every small step themselves. And it increased usability by lowering the barrier between engineering intent and CAD action.

Most importantly, it introduced a new interaction model inside Siemens NX: engineers could work with an AI that does not just answer questions, but actively helps move the design task forward.

Where This Type of Solution Fits Best

This type of hybrid agentic AI is especially relevant for organizations that:

• use Siemens NX in complex design or construction workflows

• want to reduce manual CAD interaction without losing control

• need repeatable execution with built-in validation

• want AI to assist with real work, not only with documentation or chat

• are building toward next-generation engineering environments with intelligent automation

It is particularly well suited for engineering teams where accuracy, traceability, and execution reliability matter just as much as productivity.

Conclusion

This project demonstrates what engineering AI looks like when it moves beyond conversation and into execution.

We developed a hybrid agentic AI for Siemens NX that can understand user intent, decide which tools to use, fill in parameters autonomously, execute commands inside NX, verify results, and assist engineers during construction and modeling tasks.

The outcome was not a passive assistant, but a controlled AI co-engineer designed for real CAD work.