AI Agents for Engineering Design: Real Examples, Capabilities, and How to Evaluate Them

As interest in agentic AI grows, engineering leaders are being asked to develop strategies for deploying AI agents for engineering design. Before starting an evaluation, most want to know: 

• What agents exist today
• What parts of the engineering design process they can realistically handle
• How mature these solutions actually are in production environments

This article focuses specifically on engineering design for manufactured products—not software engineering, process engineering, or civil engineering. We’ll walk through real examples of AI agents already being used in engineering design, explain how much of a workflow they can own, and outline how engineering leaders should evaluate these systems.

What Qualifies as an AI Agent for Engineering Design?

Importantly, no single AI agent can (or should) handle every phase of a phase-gate design process. AI agents must be workflow-specific, with clearly defined triggers, responsibilities, and handoff points back to human engineers. In practical terms, an AI agent for engineering design:

• Executes a multi-step workflow without requiring repeated prompting
• Operates inside a production environment, not just a chat interface
• Uses structured data (CAD, drawings, standards, simulation context)
• Produces repeatable, testable, and predictable outputs

When AI Agents Make Sense in Engineering Design

Not every engineering design workflow is a good candidate for agentic AI—at least not as a first step. The most successful deployments tend to start with workflows that align closely with what AI is inherently good at, while also addressing areas where even experienced engineering teams struggle to be perfectly consistent.

In practice, there are a few characteristics that make a design workflow especially well-suited for AI agents:

1. Workflows That Require High Consistency

Even in highly capable engineering teams, there is natural variation in how people approach the same task. Two engineers reviewing the same drawing may focus on slightly different details, interpret standards differently, or simply miss different things depending on time pressure and context.

For certain design activities, that variation is acceptable—or even desirable. But for others, consistency is the goal.

Tasks like:

• Verifying title block completeness
• Checking revision consistency
• Ensuring materials are called out the same way across pages
• Applying the same interpretation of design standards every time

These benefit from being executed the same way, every time. With the right guardrails, an AI agent can apply rules and checks far more consistently than a rotating group of humans, even a very well-trained one.

2. Workflows That Involve Large Volumes of Information

Many engineering design decisions depend on referencing large bodies of information that are difficult for humans to absorb quickly or recall reliably.

Examples include:

• Lengthy design standards and guidelines
• Supplier specifications
• Internal best practices accumulated over years
• Historical design issues and lessons learned

AI agents excel at reading and cross-referencing large volumes of data quickly. For instance, it is unreasonable for a human engineer to read hundreds of pages of design guidelines each time they review a design. But an AI agent can easily reference an entire library of design standards and guidelines, flagging the relevant ones for a specific review. In these cases, the agent doesn’t replace judgment; it accelerates understanding. The engineer still makes the final decision, but they do so with better, more complete context.

3. Workflows Performed at High Volume

Finally, the economics matter. Deploying an AI agent requires upfront investment: defining the workflow, providing context, testing outputs, and refining behavior until results are reliable.

That investment makes the most sense for workflows that are performed frequently.

For example, in large engineering organizations, drawing reviews alone can number in the tens of thousands per year. In those environments, even modest time savings or quality improvements per review compound quickly.

High-volume workflows make it possible to justify the effort required to productionize an agent—and to benefit from continuous improvement over time.

Real Examples of AI Agents for Engineering Design

Below are examples of agent types that are already being used today, not speculative future concepts.

1. CAD Review and Drawing Review Agents

One of the most mature applications of agentic AI in engineering design is design review.

Design and drawing review agents can:

• Analyze CAD models or drawings
• Identify design risks such as common DFM issues
• Flag ambiguous or incomplete drawing notes
• Check title blocks, revisions, and BOM consistency
• Compare designs against large libraries of organizational standards and guidelines

For example, CoLab’s AutoReview performs agentic CAD review and drawing review by annotating models and drawings directly, highlighting issues and explaining why they matter. These agents don’t require engineers to prompt every individual check, they understand how to perform a complete review based on predefined workflows and prompts.
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2. Simulation Setup Agents

Several simulation vendors are now introducing AI agents to streamline simulation setup. These agents can:

• Read and interpret simulation documentation
• Provide step-by-step guidance tailored to the current model
• Recommend boundary conditions, materials, or physics models based on geometry and context
• Reduce time spent on repetitive configuration tasks

Here, the agent accelerates setup and reduces friction, while engineers remain responsible for interpreting results.

3. Lessons Learned and Design Knowledge Agents

Another emerging—but already practical—application is lessons learned agents.

Traditionally, lessons learned processes rely on:

• End of program retrospective meetings
• Tracking lessons learned in spreadsheets
• Reviewing those spreadsheets at the start of the next program
• Attendees of that review meeting recalling the lesson learned when it matters

This approach introduces risk, because it’s easy to skip steps or for someone to fail to recall critical information at the right time.

AI agents can instead:

• Continuously capture design issues and feedback during reviews
• Store them in a centralized system
• Identify similarities between past and current programs
• Proactively surface relevant lessons during new design work

Because many organizations struggle to execute lessons learned consistently today, this is a low-risk, high-leverage use case for agentic AI.

How Much of the Design Workflow Can an AI Agent Own?

AI agents can already own multi-step workflows that might take a human engineer anywhere from a few minutes to an hour or more. However, most organizations should approach this kind of workflow automation with some caution. A critical question to ask is: How much risk am I taking on by automating this workflow with an AI agent?

The most successful teams take a phased, risk-aware approach, starting with low-risk, high-impact workflows and expanding from there as their organization becomes more fluent with AI:

Some engineering design workflows are low risk because, in practice, they are either inconsistently executed or not reliably executed at all today. Lessons learned is a good example. Other workflows—like design review or drawing review—are almost always performed today, which means they carry more inherent risk if something goes wrong. However, these workflows can still be safely automated if the scope of the agent is defined carefully. A pragmatic approach is to deploy AI agents as first-pass systems, rather than final decision-makers. For example:

• An agent performs an initial drawing review
• It flags potential issues such as ambiguous notes, title block inconsistencies, or standards violations
• The design owner reviews those findings and decides what to address, override, or accept

In this model, no steps are skipped, humans remain accountable for trade-offs, and the agent improves efficiency by catching basic or easily missed issues early. The result is often a cleaner design entering human review, allowing engineers to focus their time on nuanced decisions instead of basic checks.

How to Evaluate AI Agents for Engineering Design

Many vendors claim to offer “engineering AI agents,” but maturity varies widely. When evaluating solutions, engineering leaders should look beyond broad claims like “replaces a junior engineer” and ask:

• Is the agent workflow-specific, or vaguely general?
• What data does it actually have access to (CAD, drawings, standards)?
• How are system-level instructions and organizational context provided?
• Has the agent been tested and refined using real engineering data?
• How does the vendor ensure predictable, reliable outputs over time?

In practice, the most mature agents come from vendors with deep experience in the workflow itself. For example:

• Design review agents built by companies that have supported design reviews for years
• Simulation agents developed by simulation software providers

Other vendors are still exploring scope and capabilities, with less real-world feedback informing their systems.

AI agents for engineering design are no longer theoretical

They already exist, they already deliver value, and they are best applied when:

• The workflow is clearly defined
• The scope of responsibility is realistic
• Human oversight remains intentional

The most successful deployments will come from using agentic AI to improve consistency, quality, and focus across engineering design work. CoLab partners with global engineering organizations to strategically automate low risk, high impact workflows. You can learn more about our approach here.