Engineering teams have invested heavily in AI copilots — yet workflows remain slow, fragmented, and hard to scale. The real obstacle isn't automation. It's the cost of structuring engineering knowledge in the first place.
Engineering design is no longer a tool problem. It's a workflow problem.
For years, engineering teams have invested in increasingly powerful software: advanced CAD platforms, simulation environments, PLM systems, and more recently, AI-powered copilots. Yet despite these investments, the core challenges remain largely unchanged — slow design iteration cycles, fragmented toolchains, manual validation, limited design space exploration, and expert knowledge that is difficult to scale.
AI copilots have improved productivity at the margins. But they have not fundamentally transformed how engineering work gets done. The reason is simple: the real bottleneck isn't execution. It's orchestration.
Understanding the distinction between AI copilots and AI agents is essential to grasping where engineering automation is headed.
AI copilots assist engineers by generating suggestions, accelerating modeling tasks, and automating isolated actions within a single tool or step. They are reactive by nature, they wait for human input before acting, and they remain disconnected from system-level constraints and validation logic.
In practice, engineers using AI copilots are still responsible for defining design intent, navigating between tools, running validation workflows, and ensuring compliance with engineering rules. AI copilots reduce effort, but they do not reduce complexity.
AI agents in engineering operate differently. Rather than waiting for instructions at each step, AI agents are designed to interpret engineering objectives, chain multiple steps autonomously, apply structured constraints, and produce actionable results without continuous human intervention.
This is not an incremental improvement. It represents a shift from tool-based engineering to goal-driven engineering systems.
Here is where most AI initiatives in engineering stall, and where the distinction between copilots and agents becomes concrete.
Engineering knowledge is not text. It is constraints, parameters, dependencies, rules, and system architectures. For AI agents to operate reliably on this knowledge, it must be structured: broken down into modular, executable tools that an agent can query, chain, and apply with precision.
Until now, this structuring has been entirely manual. A developer works with a domain expert, extracts their know-how, translates it into workflows and algorithms, and builds the agent that executes them. This process is slow, expensive, and difficult to justify at scale.
Engineering organizations face a fundamental ROI question: is it worth the investment to digitalize this scope of knowledge? In many cases, the answer has been no, and so a large portion of engineering expertise has remained locked in the minds of experts, non-reusable and non-automatable.
This is the real bottleneck. Not the execution of workflows, but the cost of creating them.
When engineering knowledge is properly structured, workflows can be executed autonomously by AI agents rather than manually orchestrated by engineers.
A typical automated engineering verification workflow looks like this:
This approach transforms engineering verification from a manual, expert-dependent process into a repeatable, scalable one — deployable across teams and projects without starting from scratch each time.
With the introduction of Lagrangia, Dessia addresses the root problem that has prevented autonomous engineering from scaling: the cost of structuring knowledge in the first place.
Lagrangia is a DeepAgent — an AI model designed not just to execute engineering workflows, but to build them. Given a knowledge base, Lagrangia autonomously performs three functions that previously required significant developer effort:
This means engineering teams can go from a domain problem to a deployable agent dramatically faster than before, without the traditional back-and-forth between business and technical teams.
In its current version, Lagrangia 1.0 specializes in data science applied to 3D CAD: shape search, pattern recognition, distance calculation, and the generation of ephemeral geometry used to support automated verifications. A concrete example is automated screwdriver accessibility verification — where the model detects bolted assemblies in a CAD file, generates the clearance volumes on the fly, and checks for interference automatically, without a single line of code from the engineer.
The next milestone, Lagrangia 2.0, will extend these capabilities to 2D drawings, broadening coverage across design and document validation workflows.
The transition from AI-assisted to AI-orchestrated engineering is underway, but it will not happen by deploying more copilots. It requires solving the foundational problem first: making engineering knowledge structured, modular, and automatable at scale.
Lagrangia represents Dessia's answer to that problem. An infrastructure that makes autonomous engineering possible, the layer that turns expert knowledge into deployable agents, without the bottleneck of manual structuring.
The engineering organizations that will lead are not those with the most advanced individual tools. They are those who can structure their knowledge, automate the creation of their workflows, and deploy agents across new scopes faster than their competitors.
Because the real disruption in engineering is not AI generating designs. It is AI making engineering knowledge scalable.
Published on
27.04.2026
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