AI Agents in Machine Design Engineering: What They Still Can’t Do

You modify a bracket and ask a straightforward question: will it withstand the expected load? In a traditional workflow, answering that question may involve exporting geometry, requesting a specialist’s help, preparing the simulation, and waiting for results. Meanwhile, the design keeps moving.

AI agents inside engineering software aim to shorten that loop. One current example is the SimScale Engineering AI Agent, which SimScale and PTC announced as an embedded app inside Onshape at IMTS 2026 in Chicago in September 2026.

For machine design engineering, the opportunity is faster exploration—not automatic approval. An agent may help prepare and run a study, but it cannot independently establish your real operating conditions, supplier constraints, or release requirements. The useful question is where assistance saves effort, where human review remains essential, and what happens when design exploration outpaces documentation capacity.

1. What’s Changing: Agents Coordinate Simulation Workflows

The shift is from individual commands to assisted workflows. Instead of navigating every simulation step manually, an engineer can describe an objective in natural language. According to SimScale, an Onshape user can open the agent from the workspace and ask a question in a single prompt. The agent reads the active model, proposes a setup covering the applicable physics, meshing and boundary conditions, runs the analysis on SimScale’s platform, and reports the results in plain engineering language.

Those distinctions matter. Suggesting a study is not the same as selecting the right physics for an unfamiliar problem. Explaining a result is not the same as demonstrating that the result answers the actual engineering question. SimScale says users can step in at any point to question a boundary condition or override an assumption, and that each run is stored in the cloud and can be audited through its Agent API. Those are vendor-described capabilities, and they’re what makes the human checkpoints in this article practical. Note that the analysis runs on the vendor’s cloud platform, so teams should also check how their own data-handling policies apply.

Preparation is a practical starting point

Geometry cleanup and initial setup contain substantial repeatable work. Imported geometry may need repair, small features may need suppression, and loads or restraints must be associated with the correct surfaces. Assistance with these tasks can reduce the effort required to begin a study.

But cleanup must preserve the behavior being investigated. A cosmetic thread may be unnecessary in a simplified model, while a fillet near a highly loaded connection may be important. Engineers still need to understand what was removed and why.

Agents and surrogate models are different

An agent coordinates tasks; a surrogate model estimates behavior. A surrogate model uses existing simulation or experimental data to approximate a response without repeating a full analysis for every candidate. That can support rapid screening, but confidence can weaken outside the conditions represented by its data.

Engineering.com’s September 2026 coverage of surrogate models describes them as a useful substitute for costly 3D simulation once validated. It also stresses the trade-off between accuracy and the data, effort and computing time needed to train them, and the need for experienced engineers to question implausible predictions.

2. What AI-Assisted Simulation Does Well

Early-stage comparison is a useful application. When several plausible concepts can be assessed under appropriate, consistent assumptions, assisted workflows may help engineers narrow the field before committing to detailed design.

For a mounting bracket, useful questions include:

Stiffness: Does an added rib reduce deflection more effectively than increasing plate thickness?
Load path: Does relocating an attachment improve how forces pass into the supporting structure?
Material: How do candidate materials affect the result under the same loading conditions?
Sensitivity: Does the conclusion change when the mounting arrangement is represented differently?

The value is not a polished report for every option. It is better direction while changes are still relatively easy.

Routine handoffs may decrease, while specialist review remains important. Designers may be able to prepare initial studies before involving an analyst. Specialists can then focus on unfamiliar loading, difficult contacts, nonlinear behavior, fatigue, and other questions requiring deeper expertise.

Even “does this bracket survive?” needs clarification. Does success mean avoiding permanent deformation, limiting deflection, resisting repeated loading, or preventing instability? A faster workflow is useful only when the acceptance criteria are clear.

3. Where It Breaks Down: A Convincing Result Can Answer the Wrong Question

Simulation evaluates the model and conditions supplied to it. If a restraint is unrealistic or an important load is missing, a correctly executed analysis can still produce a misleading conclusion. Clear explanations and attractive stress plots do not repair incomplete assumptions.

Natural-language prompts can make that problem less visible. “Apply the normal operating load” is insufficient unless the workflow has access to an approved definition of that load.

Model quality still determines usefulness

Geometry can appear complete while containing gaps, unintended overlaps, or missing connections. Automated simplification can also alter a load path without making the change obvious.

Mesh quality the quality of the small elements used to represent the geometry—affects the solution. Engineers may need to check whether refining the mesh materially changes the result and whether localized stress peaks reflect physical behavior or modeling artifacts.

Manufacturing context must be supplied

An agent does not automatically know your production constraints. Unless reliable information is explicitly provided, it may lack your supplier’s process capabilities, available materials, preferred joining methods, or inspection limitations.

A shape that performs well in simulation may still be difficult to machine, weld, assemble, or measure. Nominal geometry also does not establish whether permitted manufacturing variation will affect fit, alignment, or load sharing.

Responsibility stays with the engineering organization

Someone authorized under the organization’s process must review the assumptions, interpret the evidence, and approve progression or release. Depending on the application, that evidence may include independent calculations, further simulation, specialist review, or physical testing.

AI assistance changes how work is performed. It does not transfer accountability.

4. The Overlooked Gap: Exploration Can Outrun Documentation

More options do not automatically mean more release-ready designs. If exploration becomes faster while model refinement and detailing capacity stay unchanged, the queue can move downstream. Teams may accumulate promising candidates without clear decisions about which versions remain active.

Not every variant needs manufacturing drawings. Every retained candidate does need enough identification and traceability to avoid confusion; selected designs need the fuller engineering package.

That package can include:

Model refinement: Clear features, resolved interfaces, and preserved design intent.
Tolerance definition: Dimensions and allowable variation based on function and assembly needs.
Drawing consistency: Agreement between each general assembly drawing, detail drawings, and the bill of materials.
Revision control: A clear record of what changed, why it changed, and which version is approved.

Geometric dimensioning and tolerancing, or GD&T, communicates permitted variation through functional relationships between features. A favorable simulation result does not automatically establish the datum scheme, fits, or inspection requirements.

The goal is therefore not to document every alternative. It is to select deliberately and document the chosen design thoroughly.

5. A Practical Framework: Explore, Review, Release

Separate exploratory results from release evidence. The following checkpoints help teams retain speed without allowing provisional assumptions to become invisible design decisions.

Stage	Appropriate assistance	Required human checkpoint
Define	Organize inputs and identify missing information	Confirm loads, materials, interfaces, operating conditions, and acceptance criteria
Explore	Prepare supported studies and compare candidates	Check that assumptions are appropriate and comparisons are meaningful
Review	Summarize differences and highlight areas of concern	Examine geometry, setup, result sensitivity, and manufacturing feasibility
Detail	Support routine information handling where available	Establish design intent, tolerances, GD&T, and document consistency
Release	Assist with completeness checks where supported	Follow the organization’s approval and revision-control process

Before advancing a candidate, ask:

Traceability: Can another engineer identify the exact model revision and analysis assumptions?
Relevance: Does the study address the actual operating conditions and likely failure modes?
Sensitivity: Do reasonable changes in uncertain inputs alter the conclusion?
Manufacturability: Have supplier, process, assembly, and inspection constraints been considered?
Documentation: Do drawings, specifications, and the bill of materials agree?
Ownership: Is responsibility for review and release clearly assigned?

Flexible capacity works best within these boundaries. Additional support is more useful when requirements, review responsibilities, and document standards are clear than when an uncontrolled folder of variants is simply handed over.

6. Where Asset-Eyes Supports the Human Engineering Layer

The next step after exploration is disciplined design and documentation. Asset-Eyes is a machine design and engineering services company supporting manufacturers and OEMs across industries. Our machine design engineering work covers the mechanical refinement and engineering information that selected concepts need.

That work includes refining models, resolving mechanical interfaces, and developing drawings and technical documentation for downstream manufacturing and assembly. Our cad drafting services form part of this broader engineering scope not a substitute for design judgment.

For teams facing fluctuating workloads, external support can add capacity around refinement and detailing while the customer’s authorized team retains approval responsibility. Our focus is on helping the right concepts progress toward manufacturable, fully documented designs.

7. Faster Exploration Still Needs Strong Engineering Foundations

AI agents may shorten the path from a design question to an initial simulation result. They cannot independently establish every real-world constraint, resolve every manufacturing trade-off, or authorize a design for release.

The practical approach is to use assistance for exploration, experienced people for judgment, and controlled documentation for downstream execution. Otherwise, faster iteration may simply move the bottleneck from analysis to detailing.

If design variants are outpacing your team’s capacity to refine and document them, Asset-Eyes can support the engineering work needed to move selected concepts toward release.

Schedule a discovery call with us today: Calendly — Welcome to Asset-Eyes Infomatics