AI copilots are transforming engineering: streamlining workflows, automating tasks and revolutionizing design processes. The future is here.
By Eric Vinchon, VP of Product at Tech Soft 3D
One of the most important technological developments to watch for 3D engineering in 2025 includes the advancement and integration of AI assistants into engineering software and the power of single and multi-agent systems.
Integrated AI-based tools are already starting to be leveraged in consumer applications. They can be found in Outlook, PowerPoint, VS Studio, Photoshop, Grammarly, Apple Intelligence and many more. These assistants leverage large language models (LLMs) to provide functionality to their users. AI tools in engineering applications will utilize much of the same technology to streamline processes, empower creativity, and lower barriers to entry.
By leveraging natural language models, engineers will be able to interact with applications directly through voice and text commands. At other times, AI-powered search tools can help engineers learn contextually, find the functionality they’re looking for, and increase productivity.
Engineers will be able to use AI assistants to automate time-consuming, repetitive tasks in CAD and other design, analysis and manufacturing software. The ability to generate scripts and automate commands without coding reduces the time and expertise needed for engineers to leverage these time-saving capabilities.
One of the biggest limitations to LLM-based tools currently is how we as users are forced to interact with them. Time spent copy/pasting text, code, data, or information grabbed from a Google search is tedious, yet often required to tailor the tools outputs to your needs.
LLMs will soon integrate directly with organizational information to cut out this time-consuming and error prone stage, with direct access to data, email and scheduling, technical documentation, API info, support requests, and more.
For engineers and developers, these integrations will let them more effectively utilize AI tools while reducing repetitive copy-pasting and prompt iteration. They will be able to generate code and ensure it compiles without constant user modification. In support cases, they can reproduce reported issues. Other parts of work will be more convenient, with assistants generating customized reports, design reviews, analysis, and meeting prep with all the relevant info you need.
The underlying technology of these tools is still largely in its infancy, and their development is hugely exciting. As they get access to more detailed, user-specific information, their utility will become similarly tailored to the needs of the organizations leveraging them.
Whether in your personal or professional lives, we’ve all had a terrible experience with automated customer service. Most bots simply can’t properly understand natural language or context, generate unique responses, or generally tailor their response in any way beyond a limited number of responses.
AI agents are different, and poised to revolutionize support, onboarding, and much more. These autonomous programs take on tasks from users and complete them independently. By leveraging LLMs, they create and execute action plans based on user needs, often interacting directly with an organization’s tools and data. This ability to understand context and work autonomously is a key part of what makes them so valuable.
In the year ahead, these agents are poised to become one of the most transformative developments in AI, with significant potential to dramatically enhance the experiences of engineers and developers.
Simple AI agents can handle straightforward, repetitive tasks such as answering FAQs, guiding users through standard onboarding procedures, or assisting with account setup. They can also improve the quality of service by offering immediate assistance to users across different time zones, reducing wait times, and increasing customer engagement.
Modern engineering requires complex software, and its users routinely have unique, context-specific questions related to both onboarding and everyday use. AI agents can better understand these queries, interpret user inquiries, and access relevant information from documentation, knowledge bases, and help desk systems.
Multi-agent systems can handle more complex tasks and offer dynamic, personalized assistance. Specialized agents can collaborate to resolve intricate issues, ensuring that even the most complex customer needs are met efficiently. One agent might diagnose a technical problem, another could retrieve relevant documentation, and a third might initiate a follow-up procedure. This coordinated effort enhances problem-solving capabilities and improves the overall customer experience.
By assigning different agents to address various aspects of a user’s needs, multi-agent systems will provide highly personalized support that adapts in real-time to individual preferences and requirements.
Multi-agent systems can adjust to new challenges by adding or modifying agents. This flexibility allows organizations to maximize support without overhauling their infrastructure. Each agent in the system learns from interactions and shares insights with others, enhancing overall performance. This continuous learning leads to ongoing improvements in service quality and operational efficiency.
A sales team leverage could create and leverage a multi-agent system to help them answer the question: “Find me all the small to medium sized manufacturing shops in Germany who are automotive suppliers.” The agents would pull information from a wide range of sources – general search, show/event information, specialized publications and magazines, databases, LinkedIn, etc. The information would be validated, with duplicates removed and fact-checked, then cross-referenced with your own CRM information.
This would be accomplished by not one but a series of specialized agents working together. A web search agent, an event screener, a validation tool, a CAD expert. This is a task that would take a human several hours, and they could never realistically search as thoroughly as possible with these tools.
Embracing these technologies is crucial for organizations aiming to enhance user experiences and stay competitive in the evolving digital landscape. We expect to see huge investment in developing AI agent systems across a wide array of industries.
2025 will see AI-powered agents and assistants revolutionize their role in 3D engineering. Tech Soft 3D strives to be a leader in this space and will continue to invest in this and other emerging, transformative technologies.
About the Author:
Eric Vinchon, Vice President of Product Strategy at Tech Soft 3D, brings over 20 years of experience in software engineering and product management. He began his career at TTF in 2001 as a Software Engineer, later joining Adobe in 2006 as a Senior Software Engineer. In 2011, Eric joined Tech Soft 3D, initially serving as Director of Engineering, where he oversaw product development. Promoted to Director of Products in 2015, he further refined the company’s product offerings before assuming his current role in 2017, where he leads strategic product initiatives. Eric holds a Diplôme d’ingénieur in Mechanical Engineering and Development from INSA Lyon (1998–2001).
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