A.Engineer, a new agentic AI platform designed to help civil and structural engineers automate calculations and reporting, has launched in Europe.

Developed by Tyréns NEXT, the innovation arm of engineering consultancy Tyréns Group, A.Engineer combines engineering data, calculation tools, and report generation into a single “intelligent workspace”.

The platform is designed to simplify workflows, allowing engineers to spend less time on manual tasks and more time on creative design, instructions, and quality assurance.

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According to the developers, A.Engineer is built the principle that engineering is empowered by AI yet always verified by a professional user.

The platform provides a full audit trail for every step the AI takes. Engineers can see all data inputs, outputs, underlying code, and the reasoning behind each decision. There are no outputs without verification.

A.Engineer includes an Agentic Calculation Tool Builder, where engineers can upload their own data, Excel tools, or connect to legacy systems, and the system then generates the necessary calculation tools — including both the code and the user interface — “in minutes”.

Meanwhile, an Agentic Report Builder connects data and verified calculations to “automatically build” professional reports with tables, graphs, summaries, and visualisations.

The platform can integrate (via MCP connection) with established engineering tools such as Revit, Sparkel, ETABS, SAP2000, and Strusoft.

According to Richard Parker, senior structural engineer at AKT II and product lead for A.Engineer, early users are already seeing major efficiency gains, “Our research shows that 40–80% of engineering work is still manual,” he said. “With A.Engineer, we can automate over half of those manual tasks. A calculation report that might have taken half a day can now be done in under an hour. Engineers working side by side with A.Engineer deliver world‑class results in record time.”

Europe, the home market of the Tyréns Group, will be the first region to gain access to A.Engineer. A global rollout of A.Engineer is scheduled for Q1/2026.

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Oldcastle Infrastructure has introduced CivilSense ROI Calculator, a digital tool designed to help municipalities and utilities make informed, data-driven decisions for water infrastructure projects.

The CivilSense ROI Calculator supports municipal water management decisions by revealing hidden water losses within the supply network and demonstrating the long-term financial benefits of building sustainable, resilient water systems.

Supporting the CivilSense platform, the ROI Calculator addresses critical challenges in water management by quantifying the likely impacts of aging infrastructure.

It allow users to calculate the significant costs associated with non-revenue water (NRW) losses, project probable leak frequencies and economic consequences of water main breaks based on system characteristics, and model the long-term value of infrastructure investments.

According to the developers, it provides a valuable overview of the financial impact, strengthening the business case for necessary upgrades to aging water infrastructure and new projects.

“Addressing our nation’s aging water infrastructure requires not just innovative products, but also smarter, more transparent planning tools,” said Peter Delgado, business development manager at Oldcastle Infrastructure. “The ROI Calculator is a direct response to this need. By clearly demonstrating the long-term financial benefits of sustainable and resilient water systems, we are empowering our partners to build stronger, more reliable communities for the future.”

Meanwhile, learn more about CivilSense in this AEC Magazine article.

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Jacobs has introduced Flood Platform, a cloud-hosted hub for advanced flood modelling that is designed to support the planning and delivery of critical flood infrastructure programs.

Flood Platform is designed to help firms overcome the challenge of managing and interpreting large volumes of data, especially as flooding and extreme weather events become more frequent and severe.

The subscription-based offering, built on Microsoft Azure technology, standardises how users manage, view and analyse flood-related data.

It acts as a central location for data, simulations and collaboration, and integrates with flood modelling tools like Jacobs Flood Modeller.

Users can upload data, manage access permissions, perform analysis, and collaborate with both internal teams and external stakeholders.

“At Jacobs, we integrate digital, data and AI capabilities throughout our global delivery portfolio to solve complex client challenges,” said Jacobs executive vice president Amer Battikhi.

“Flood Platform demonstrates how cloud-based technology and automation can improve infrastructure planning and modelling – helping clients make more informed decisions and achieve faster project delivery and strengthened resilience.”

Flood Platform was originally developed by Jacobs for internal use and has supported project delivery for more than 15 years. In response to client needs, Jacobs refined and expanded its capabilities, and it is now available as a subscription service to organizations worldwide.

The platform has been instrumental in supporting significant projects such as Melbourne Water’s Flood Mapping Program, which aims to provide comprehensive flood mapping for all municipalities across Greater Melbourne and the Westernport region, and the Environment Agency’s Oxford-Cambridge Arc Flood Risk Investment Study, which helped unlock more than $134 billion (£100 billion) in economic value.

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The Transcend story begins inside Organica Water, a company based in Budapest, Hungary and specialising in the design and construction of wastewater treatment facilities.

Transcend was a tool built by engineers at Organica to solve the persistent headache of producing preliminary designs for these facilities quickly and at scale. They found traditional manual design processes too limiting, so they put together a digital tool that connected spreadsheets, calculations and process logic in order to automate much of the work associated with early-stage design.

This tool, the Transcend Design Generator (TDG), was a big success at Organica, slashing the time it took engineers to produce proposals and enabling them to explore multiple design scenarios side-by-side.

By 2019, it was clear that while Transcend may have started off as an internal productivity aid, it had matured sufficiently to represent a significant business opportunity in its own right. Transcend was spun off as an independent company, led by Ari Raivetz, who served as Organica CEO between 2011 and 2020.

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Today, TDG is positioned as a generative design and automation solution for the infrastructure sector, targeted at companies building critical infrastructure assets such as water and wastewater plants and power stations. It is billed as accelerating the way that such facilities are conceived, embedding sustainability and resilience into designs from their earliest stages.

Among Transcend’s strategic partnerships is one with Autodesk, which sees TDG integrated with mainstream BIM workflows, providing a bridge between early engineering and detailed designs. Autodesk is also an investor in Transcend, having contributed to its 2023 Series B funding round. To date, Transcend has raised over $35 million and employs some 100 people globally.

A look at Transcend’s tech

A wealth of capability is baked into the TDG software, which goes beyond geometry generation and parametric modelling to also embrace process engineering, civil and electrical logic, simulation and cost modelling.

Engineers enter a minimal set of inputs, such as site characteristics, flow rates and regulatory requirements, and the tool generates complete conceptual designs that are validated against engineering rules. Outputs include models, drawings, bills of quantities, schematics, cost estimates and carbon footprint calculations. Every decision and iteration is tracked, producing an audit trail that would be difficult to achieve in manual workflows.

The difference compared to traditional design practices is quite stark. With manual conceptual design, weeks of work may yield only one or two viable options, locking in assumptions before alternatives can be properly tested.

Transcend compresses this process into hours, producing multiple design variants that can be compared quickly and objectively. Because the data structures and outputs are already aligned with BIM and downstream processes, the work does not need to be redone at the detailed design stage.

Transcend executives say that using TDG on a project creates a shift from reactive, labour-intensive conceptual engineering to a more proactive approach. The tool, they claim, is capable of delivering part of a typical initial design package, with outputs detailed enough to support option analysis, secure stakeholder approval, underpin bids and provide reliable cost and carbon estimates.

The intent, however, is not to replace detailed design teams. Instead, it is to accelerate and standardise the slowest stage of the workflow, so that engineers can move into the final stage of detailed design with a far clearer, validated baseline.

Impressively transdiscipline

TDG is very much a BIM 2.0 product for civil/infrastructure design and is, at its heart, generative design software.

It uses rules-based automation and algorithms to generate early-stage models, drawings and documentation, solving complex engineering problems through auditable, traceable data, rather than relying on less-reliable LLMs.

All TDG’s processing is on the cloud, so it works without the need of a desktop application and can be accessed from any device with a web browser.

We also find it to be impressively transdiscipline, integrating the design processes of mixed teams to produce complete, multi-option design packages that reflect the work and experience of mechanical, civil and electrical design experts.

This end-to-end, multidisciplinary approach certainly appears to be a key differentiator for Transcend in the automation space.

Q&A with Transcend co-founder Adam Tank

Adam Tank is co-founder and chief communications officer at Transcend. AEC Magazine met with Tank to focus on the company’s Transcend Design Generator (TDG) tool and hear more about its future product roadmap.

AEC Magazine: To begin, we’re curious to know how you define TDG, or Transcend Design Generator, Adam. Is it a configurator, is it AI, is it both – or is it something else entirely?

Adam Tank: TDG is fundamentally a parametric design software. While people often mistake sophisticated autoutomation for artificial intelligence, our software is built on processes that are really thought-out. It operates as a massive parametric solver, similar to tools used in site development like TestFit, but applied to multidisciplinary engineering for critical infrastructure.

We utilise rules-based automation and algorithms to generate complete, viable design options, based on inputs, constraints and standards. TDG can produce designs quickly, by combining first-principles engineering, parametric design rules and proprietary data sets.

Our primary focus is on solving complex engineering problems through auditable, traceable data, rather than relying solely on large language models that might hallucinate. Every decision the software makes can be traced back to a literal textbook calculation or a rule of thumb provided by an expert engineer.

AEC Magazine: So what exactly does the output for a project produced by TDG look like and how deep does the generated geometry go?

Adam Tank: TDG supports the entire early-stage design process. The software is built to follow the same sequential workflow as a multi-disciplinary engineering team, beginning with process calculations, then moving on to mechanical, electrical and civil calculations.

Consequently, it is capable of generating a comprehensive set of validated, reusable data sets and outputs. These outputs include PFDs (process flow diagrams), BOQs (bills of quantities), and full P&IDs (piping and instrumentation diagrams), because it captures all the required data, such as the full equipment list, the geometry, the motor horsepower rating and the electrical consumption of the equipment.

These schematics can be produced in either AutoCAD or Revit. TDG also produces 3D BIM files with geometry generated at LOD 200. This includes key components like slabs, walls, doors, windows, concrete quantities and steel structures. LOD 200 is sufficient for the conceptual design phase, enabling teams to determine the total capital cost of a project within a 10% to 20% margin.

Furthermore, Transcend also generates drawings from the model. Because the model geometry is guaranteed to be accurate through automation, starting from precise specifications rather than attempting to fix poor modelling errors in the drawings, the resulting drawings can be relied upon.

AEC Magazine: So how does TDG effectively combine knowledge and requirements of multiple engineering disciplines into one unified solution?

Adam Tank: The key to TDG is that it functions as an end-to-end, multi-disciplinary, first-principles engineering automation tool. We built the software to follow the exact same sequential thought process that a multidisciplinary team of engineers uses today.

The process begins with the software taking user inputs regarding location, desired consumption, and facility requirements, and combining this with first principles engineering, parametric design rules, and proprietary data sets. Critically, every decision the software makes can be traced back to a textbook calculation or an engineer’s rule of thumb, providing the auditable, traceable data required in this high-risk industry.

The engine then executes the workflow. It starts with the process set of calculations. Once that data is validated, the software transfers that data to the next stage, flowing through a mechanical engine that handles the calculations and then subsequently translating the data for electrical and civil engineering needs.

Essentially, TDG integrates process, mechanical, civil and electrical design logic into one tool, acting as an engine that ‘chews it all up’, from a multi-disciplinary perspective, and produces the unified outputs required by engineers.

This complex system handles local and regional standards, equipment standards and regulatory constraints, guaranteeing that the design options generated are viable and grounded in real engineering standards.

AEC Magazine: The process certainly sounds heavily automated – but where, specifically, does TDG use AI today and what are the company’s future plans for incorporating more AI into the tool?

Adam Tank: Currently, the only part of our software that uses AI is the site arrangement, where we employ an evolutionary algorithm to optimise site layout. When a user inputs the parcel of land and specifications, the software checks constraints and runs through thousands of combinations to determine the optimal arrangement. This algorithm optimises site footprint, while taking into consideration required ingress/egress points for power and water, traffic flow and other necessary clearances.

For future AI development, we are focused on applications that build user trust and enhance productivity. For example, while TDG already produces a preliminary engineering report as part of its output package, we are looking at leveraging AI for text generation within this report.

There’s also scope for an engineering co-pilot. We’d like to integrate an AI-powered co-pilot that guides the user through the TDG interface and, critically, explains the reasoning behind the software’s design decisions. Engineers are accustomed to manipulating every variable manually, so when the computer generates the solution, they need to understand why certain components are placed the way they are. This co-pilot could quote bylaws, manufacturer limitations or engineering standards, effectively allowing the user to query the model itself.

AEC Magazine: How does Transcend handle the complexity of standards and multi-disciplinary data flow across separate but collaborating engineering functions?

Adam Tank: Our software must handle local and regional standards, equipment standards and regulatory constraints, so the amount of data collection is immense.

The complex engine we have built follows the standard engineering workflow. It starts with a user inputting project data, like location, water flow, desired treatment, existing site conditions. This data is used by the process engineer calculation models, which run sophisticated simulations to predict kinetics and mathematics.

TDG acts as the multi-disciplinary engine. It feeds data into those process models, takes the output and then translates it into the next required discipline—mechanical, then electrical, then civil.

This means the engineering itself is still being done, but our engine chews up all the multi-disciplinary requirements and produces the unified outputs that engineers require.

AEC Magazine: Into which markets does Transcend hope to expand next – and why hasn’t the company so far sought to offer higher levels of detail, such as LOD 300 and LOD 400?

Adam Tank: Our focus has been to remain the only company offering end-to-end, multi-disciplinary, first principles engineering automation for critical infrastructure. We don’t have a direct competitor, because our competition is scattered across specialised automation tools that only handle specific parts of the process, such as MEP automation or architectural configuration. We were purpose-built specifically for water, power and wastewater infrastructure, and we are the only generative design software focused entirely on these complex sectors.

Regarding LODs, we have made a deliberate strategic decision not to pursue higher LOD specifications. In the conceptual design phase, we generate geometry at LOD 200. The time and complexity required to achieve that depth would divert resources from attracting new clients and expanding into new conceptual design verticals.

If it were entirely up to me, the next big market we would pursue is transportation, covering roads and bridges, which represents a massive market in terms of total design dollars spent, eclipsing water and wastewater by almost double.

We also get asked a lot about data centre design. This expansion is technologically feasible for us. For instance, early in our company history, we developed a similar rapid configuration tool for Black & Veatch to design COVID testing facilities during the pandemic. We see a potential natural fit with companies like Augmenta, which specialises in electrical wiring automation, where we could automate the building structure and they could handle the wiring complexity.

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In late September 2025, thousands of residents in Novi, Michigan and the surrounding area were forced to contend with days of disruption following a severe water main break. Some homes and businesses faced total water outages. Others were issued a ‘boil water advisory’, recommending they boil the water from their taps due to potential contamination that might make it unsafe to drink.

This kind of scenario is all too common and causes real headaches for communities. It’s hugely damaging for utility providers, too. Alongside the significant costs of a major repair job, they are also likely to experience an angry backlash from customers and, in some cases, financial penalties from regulators.

But even more problematic for utilities is the impact of slow but steady leakage of water from their networks. According to estimates from strategy firm McKinsey, some 14% to 18% of total treated potable water in the US is lost through leaks before it even reaches customers. In England, the figure is around 19%, according to a 2024 report by the UK Environment Agency. In other words, it simply makes sound business sense to catch and fix small leaks before they lead to major problems.

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Technology can help, but to tackle leaks effectively, utilities need to take a two-pronged approach to its deployment, according to Peter Delgado, director of commercial excellence at Oldcastle Infrastructure, which is part of CRH, a global provider of building materials for transportation and critical infrastructure.

Says Delgado: “You need prediction technology, so that you know where leaks are most likely to occur across the many miles of network that you manage. And you need leak detection technology that enables you to pinpoint the location and size of leaks.”

Oldcastle Infrastructure’s CivilSense solution, he claims, is the only solution to enable customers to adopt this bilateral approach and address both sides of the coin. To do so, Oldcastle Infrastructure relies on whitelabelling technology from two other companies and combining these with its CivilSense software platform.

First, CivilSense uses AI-driven predictive analysis from Boston, Massachusetts-based VODA.ai to flag sections of a network that are at higher risk of pipe failure or breakage, based on its analysis of geographic information systems (GIS), climate and infrastructure asset data. These analyses include the ranking of different areas of often vast water networks by risk.

Next comes the deployment by frontline teams of acoustic sensors from Bicester, UK-based FIDO Tech. These sensors detect, locate and size actual leaks in real time and are magnetically attached to valves via manholes in areas of particular concern for a monitoring period typically lasting a day or two, but sometimes up to a week, Delgado says. They are engineered to ‘listen’ for the particular sounds and vibrations produced by a leaking pipe and often at levels far beyond the limits of human hearing and AI-driven analysis of that data can pinpoint a leak to an accuracy of around three metres, says Delgado. That data is also fed back into CivilSense.

Finally, CivilSense is the platform where both types of intelligence – predictive analytics and leak detection – are aggregated and visualised for utility workers, in the form of dashboards and maps accessible from any device, including the smartphones and tablets typically used by workers in the field. In this way, utilities can respond proactively to leaks before they become severe, prioritise repairs, allocate frontline resources to repair jobs and plan preventative maintenance – not to mention avoid the cost, waste and disruption of digging in a location where no leak actually exists.

It’s important to remember that much of this infrastructure is hidden away, deep underground and that’s part of the problem, says Delgado. “The ‘out-of-sight, out-of-mind’ nature of leaks can lead to a reactive mindset, but that’s no solution to the challenges that utilities increasingly face.” Water systems are ageing in the US and many other countries, he points out, with infrastructure such as pipes and valves fast approaching the end of its shelf-life.

Couple that with the impacts of rising demand for water among consumers, extreme weather events and ageing workforces in which many skilled utilities engineers are nearing retirement, he says, and you’ve got a perfect storm that simply demands a more proactive mindset.

“Without more innovative solutions, without new technologies, the current approaches used by utilities providers to deal with leaks will soon prove hopelessly inadequate,” he warns.

Indeed, if you ask the people of Novis, Michigan, they would probably tell you that the utilities industry reached that point some time ago.

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Allsite.ai, an AI-powered design platform built for civil engineering to deliver surface-ready designs in AutoCAD Civil 3D, has expanded into the North American market.

Allsite.ai uses AI Agents to automate grading, roading, drainage, and utility design, producing final- surface-ready outputs that meet local standards. Designs can be optimised for both cost and environmental resilience.

Allsite.ai runs on GPU clusters, executing billions of operations to optimise the site, balancing cut and fill, keeping pads buildable and placing retaining walls where needed. Results generated by the platform load straight into Civil 3D.

“Civil engineering firms are under pressure to do more with less,” said Sam Blackbourn, co-founder of Allsite.ai, headquartered in Auckland, New Zealand.

“We built Allsite.ai to handle the complexity of large site design, giving firms the ability to save millions while freeing engineers to focus on high- value work.”

The platform has already been tested on major projects abroad. On a 400-acre development in California, Allsite.ai helped optimise 11 miles of roads, 23 detention ponds, and 1,500 lots while balancing 650,000 cubic yards of earthworks. According to the company, designs that typically took weeks were completed in days.

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Sensat, a visualisation platform designed to bring real-world context to infrastructure projects, has formed a partnership with Transcend, a generative-design engine for water, wastewater, and power facilities.

The two companies have developed an optimised workflow designed to automate early-stage engineering in Transcend, then stream ‘highly detailed’ conceptual BIM models into Sensat for contextual review, with feedback pushed back into Transcend. Everything is synchronised through Autodesk Construction Cloud (ACC).

“Design automation only proves its worth when you can see each option on the ground and adapt in real time,” said Harry Atkinson, CCO & co-founder of Sensat. “By fusing Transcend’s generative engine with Sensat’s digital twin, owners and contractors gain the clarity to de-risk projects long before shovels hit the soil.”

“Integrating Transcend’s data-rich engineering outputs inside Sensat means teams can generate, validate and iterate designs for water plants or substations in a single afternoon—delivering infrastructure that’s not just technically sound, but genuinely constructible, affordable and sustainable,” added Adam Tank, co-founder & CCO at Transcend.

Severn Trent Water, one of the UK’s largest water and wastewater service providers, has piloted the Sensat-Transcend workflow on its Westwood Brook treatment-plant project. According to Sensat, it has helped cut design-review time by 90 percent and enabled engineers to generate dozens of treatment-plant layouts in a morning, not weeks, then visualise each option against live topography to spot constructability clashes instantly.

The workflow integration begins by defining the site within Sensat, where users can select asset footprints against a backdrop of 3D terrain, utilities, and constraint layers such as flood zones. These inputs are then used to generate multiple design options in Transcend, complete with Revit models, CAD drawings, and reports. All models and associated metadata are automatically transferred to the project’s Autodesk Construction Cloud (ACC) folder for governance and version control.

Sensat serves as the visualisation and collaboration hub, allowing stakeholders to explore the design in its real-world context, identify risks and opportunities, and capture field notes directly within the platform. Feedback is then pushed back to Transcend, where revised design options are generated—often within minutes.

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Trimble has announced a new integration between its B2W Track and Trimble Siteworks software systems to automate and enhance progress quantity tracking for earthwork and civil contractors.

This site-to-office connection is designed to enable contractors to compare actual material production quantities achieved to planned quantities more easily and accurately.

“Civil contractors must continuously evaluate how well projects are progressing against budgets, timelines and productivity goals,” said John Sheedy, director of product management at Trimble.

“Relying on phone calls, forms, emails and other disconnected communications tools to report on production quantities limits timeliness and accuracy of that reporting. This new software integration brings automation to the process to increase efficiency, eliminate errors and provide an auditable progress trail for billable milestones.”

According to Trimble, the new progress-to-plan reporting workflow allows project managers to create requests for quantity measurements — such as the amount of material added, moved or removed at a site — within the B2W Track performance tracking application.

Those requests are relayed automatically to personnel in the field who use Siteworks software with a view to fulfilling the request at ‘survey-grade accuracy’ and send the data back to B2W Track via Wi-Fi or cellular connections.

B2W Track users can then review and validate the data and reconcile it with information from other sources such as field logs. Production quantity data can also be transferred from B2W Track to the Trimble Viewpoint Vista, Viewpoint Spectrum accounting systems, or to third-party construction accounting systems.

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As a structural engineer, I’ve watched how AI is transforming various industries with excitement. But I’ve also noticed our field’s hesitation to adopt these technologies — and for good reason. We deal with safety-critical systems where reliability is a requirement.

In this article, I’ll show you how we can harness AI’s capabilities while maintaining the reliability we need as engineers. I’ll demonstrate this with an AI agent I created that can interpret truss drawings and run FEM analysis (code repository included), and I’ll give you resources to create your own agents.

The possibilities here have me truly excited about our profession’s future! I’ve been in this field for years, and I haven’t been this excited about a technology’s potential to transform how we work since I first discovered parametric modelling.

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What makes AI agents different?

Unlike traditional automation that follows fixed rules, AI agents can understand natural language, adapt to different situations, and even solve problems creatively. Think of them as smart assistants that can understand what you want and get it done.

For example, while a traditional Python script needs exact coordinates, boundary conditions, and forces to analyse a truss, an AI agent can look at a hand-drawn sketch or AutoCAD drawing and figure out the structure’s geometry by itself (see image below). It can even request any missing information needed for the analysis. This flexibility is powerful, but it also introduces unpredictability — something we engineers typically try to avoid.

The rise of specialised AI agents It’s 2025, and you’ve probably heard of ChatGPT, Claude, Llama, and other powerful Large Language Models (LLMs) that can do amazing things, like being incredibly useful coding assistants. However, running these large models in production is expensive, and their general-purpose nature sometimes makes them underperform in specific tasks.

This is where specialised agents come in. Instead of using one large model for everything, we can create smaller, fast, focused agents for specific tasks — like analysing drawings or checking building codes. These specialised agents are:

• More cost-effective to run
• Better at specific tasks
• Easier to validate

Agents are becoming the next big thing. As Microsoft CEO Satya Nadella points out, “We’re entering an agent era where business logic will increasingly be handled by specialised AI agents that can work across multiple systems and data sources”.

For engineering firms, this means we can create agents that understand our specific workflows and seamlessly integrate with our existing tools and databases.

The engineering challenge

Here’s our core challenge: while AI offers amazing flexibility, engineering demands absolute reliability. When you’re designing a bridge or a building, you need to be certain about your calculations. You can’t tell your client “the AI was 90% sure this would work.”

On the other hand, creating a rule-based engineering automation tool that can handle all kinds of inputs and edge cases while maintaining 100% reliability is a significant challenge. But there’s a solution.

Bridging the gap: reliable AI agents

We can combine the best of both worlds by creating a system with three key components (see image below):

1. AI agents handle the flexible parts – understanding requests, interpreting drawings, and searching for data.
2. Validated engineering tools perform the critical calculations.
3. Human in the loop: You, the engineer, maintain control — verifying data, checking results, and approving modifications.

Let me demonstrate this approach with a practical example I built: a truss analysis agent.

Engineering agent to analyse truss structures

Just as an example, I created a simple agent that calculates truss structures using the LLM Claude Sonnet. You give it an image of the truss, it extracts all the data it needs, runs the analysis, and gives you the results.

You can also ask the agent for any kind of information, like material and section properties, or to modify the truss geometry, loads, forces, etc. You can even give it some more challenging problems, like “Find the smallest IPE profile so the stresses are under 200 MPa”, and it does!

The first time I saw this working I couldn’t help but feel that childlike excitement engineers get when something cool actually works. Here is where you start seeing the power of AI agents in action.

It is capable of interpreting different types of drawings and creating a model, which saves a lot of time in comparison with the typical Python script where you would need to enter all the node coordinates by hand, define the elements and their properties, loads, etc.

Additionally, it solves problems using information I did not define in the code, like the section properties of IPE profiles or material properties of steel, or what is the process to choose the smallest beam to fulfil the stress requirement. It does everything by itself. N.B. You can find the source code of this agent in the resources section at the end.

In the video below, you can see the app I made using VIKTOR.AI

How does it work: an overview

Now let’s look behind the screen to understand how our AI agent works, so you can make one yourself.

In the image below you can see that in the centre you have the main AI agent, the brains of the operation. This is the agent that chats with the user and accepts text and images as input.

Additionally, it has a set of tools at its disposal, including another AI Agent, which it uses when it believes they are needed to complete the job:

• Analyse Image: AI Agent specialised in interpreting images of truss structures and returning the data needed to build the FEM model.
• Plot Truss: A simple Python function to display the truss structures.
• FEM Analysis: Validated FEM analysis script programmed in Python.

The Main agent

The Main agent is powered by Claude 3.7 Sonnet, which is the latest LLM provided by Anthropic. Basically, you are using the same model you are chatting with when using Claude in the browser, but you use it in your code using their API, and you give the model clear guidelines on how to behave and provide it with a set of tools it can use to solve problems.

You can also use other models like ChatGPT, Llama 3.x, and more, as long as they support tool calling natively (using functions). Otherwise, it gets complicated to use your validated engineering scripts.

For example, here’s how we get an answer from Claude using Python (see image below).

Let’s break down these key components:

• SYSTEM MESSAGE: This is a text that defines the agent’s role, behaviour guidelines, boundaries, etc.
• TOOLS_DESCRIPTION: Description of what tools the agent can use, their input and output.
  messages: This is the complete conversation, including all previous user and assistant (Claude) messages, so Claude knows the context of the conversation.

Tools use

One of the most powerful features of Claude and other modern LLMs is their ability to use tools autonomously. When the agent needs to solve a problem, it can decide which tools to use and when to use them. All it needs is a description of the available tools, like in the image below.

The agent can’t directly access your computer or tools — it can only request to use them. You need a small intermediary function that listens to these requests, runs the appropriate tool, and sends the results back. So don’t worry, Claude won’t take over your laptop… yet

The Analyse image agent

Here’s a fun fact: the agent that analyses truss images is actually another instance of Claude! So yes, we have Claude talking to Claude (shhh…. don’t tell him ). I did this to show how agents can work together, and honestly, it was the simplest way to get the job done.

This second agent uses Claude’s ability to understand both images and text. I give it an image and ask it to return the truss data in a specific JSON format that we can use for FEM analysis. Here is the prompt I use.

I’m actually quite impressed by how well Claude can interpret truss drawings right out of the box. For complex trusses, though, it sometimes gets confused, as you can see in the test cases later.

This is where a specialised agent, trained specifically for analysing truss images, would make a difference. You could create this using machine learning or by fine-tuning an LLM. Fine-tuning means giving the model additional training on your specific type of data, making it better at that task (though potentially worse at others).

Test case: book example

The first test case is an image of a book (see image below). What’s interesting is that the measurements and forces are given with symbols, and then the values are provided below. You can also see the x and y axis with arrows and numbers, which could be distracting.

The agent did a very good job. Dimensions, forces, boundary conditions, and section properties are correct. The only issue is that element 8 is pointing in the wrong direction, which is something I ask the agent to correct, and it did.

Test case: AutoCAD drawing

This technical drawing has many more elements than the first case (see image below). You can also see many numerical annotations, which could be distracting.

Again, the agent did a great job. Dimensions and forces are perfect. Notice how the agent understands that, for example, the force 60k is 60,000 N. The only error I could spot is that, while the supports are placed at the correct location, two of them should be rolling instead of fixed, but given how small the symbols are, this is very impressive. Note that the agent gets a low-resolution (1,600 x 400 pixel) PNG image, not a real CAD file.

Test case: transmission tower

This is definitely the most challenging of the three trusses, and all data is in the text. It also requires the agent to do a lot of math. For example, the forces are at an angle, so it needs to calculate the x and y components of each force. It also needs to calculate x and y positions of nodes by adding different measurements like this: x = a + a + b + a + a.

As you can see in the image below, this was a bit too much of a challenge for our improvised truss vision agent, and for more serious jobs, we need specialist agents. Now, in defence of the agent, the image size was quite small (700 x 600 pixels), so maybe with larger images and better prompts, it would do a better job.

An open-source agent for you

I’ve created a simplified version of this agent that demonstrates the core concepts we’ve discussed. This implementation focuses on the essential components:

• A basic terminal interface for interaction
• Core functionality for truss analysis
• Integration with the image analysis and FEM tools

The code is intentionally kept minimal to make it easier to understand and experiment with. You can find it in this GitHub repository. This simplified version is particularly useful for:

• Understanding how AI agents can integrate with engineering tools
• Learning how to structure agent-based systems
• Experimenting with different approaches to truss analysis

While it doesn’t include all the features of the full implementation, it provides a solid foundation for learning and extending the concept. You can use it as a starting point to build your own specialised engineering agents. See video below.

Conclusions

After building and testing this truss analysis agent, here are my key takeaways:

1) AI agents are game changers for engineering workflows

• They can handle ambiguous inputs like hand-drawn sketches
• They adapt to different ways of describing problems
• They can combine information from multiple sources to solve complex tasks

2) Reliability comes from smart architecture

• Let AI handle the flexible, creative parts
• Use validated engineering tools for critical calculations
• Keep engineers in control of key decisions

3) The future is specialised

• Instead of one large AI trying to do everything
• Create focused agents for specific engineering tasks
• Connect them into powerful workflows

4) Getting started is easier than you think

• Modern LLMs provide a great foundation
• Tools and APIs are readily available
• Start small and iterate

Remember: AI agents aren’t meant to replace engineering judgment — they’re tools to help us work more efficiently while maintaining the reliability our profession demands. By combining AI’s flexibility with validated engineering tools and human oversight, we can create powerful solutions that respect our professional responsibilities.

I hope you’ll join me in exploring what’s possible!

Resources

• Simple Truss Analysis agent Project repository: GitHub
• Claude: Learn promping, tool calling and multimodality (free course)
• LLama 3.2: Learn promping, tool calling and multimodality (free course)
• Claude documentation on tool use
• Claude documentation on vision

About the author

Anande Bergman is a product strategist and startup founder who has contributed to multiple successful tech ventures, including a globally-scaled engineering automation platform.

With a background in aerospace engineering and a passion for innovation, he specialises in developing software and hardware products and bringing them to market.

Drawing on his experience in both structural engineering and technology, he writes about how emerging technologies can enhance professional practices while maintaining industry standards of reliability.

The post AI agents for civil engineers appeared first on AEC Magazine.

In the world of infrastructure design, traditional processes have long been plagued by inefficiencies and fragmentation. That’s the view of engineer turned software developer Andreas Bjune Kjølseth, CEO of Norwegian startup Infraspace. “Going from an idea to actually having a decision basis can be a quite tedious process,” he explains.

Four years ago, Kjølseth left his career in civil engineering consulting and founded Infraspace, to develop a brand new generative design tool for civil infrastructure alignments – road, rail or power networks. In his years as an engineer and BIM manager Kjølseth was left frustrated by the limitations of traditional processes. Civil engineers commonly must navigate multiple software tools, explains Kjølseth – sketching in one platform, generating 3D models in another, using GIS for analysis on land take and environmental impact, and then manually assembling, comparing and presenting alternatives.

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Infraspace aims to unify this fragmented workflow within a single, cloudbased platform. The software is primarily designed to tackle the initial phases of linear civil infrastructure projects, using an outcome-based approach, as Kjølseth explains. “Users can define where they want the generative AI engine to explore alternatives and define the outcomes, such as, ‘I want options with the least possible construction costs, shortest travel time or length, and the least land take in certain areas.’ Then the algorithm will quickly explore opportunities to make better solutions.”

The Infraspace cloud platform generates thousands of alternatives within minutes, enabling engineers to explore options they might not have considered if done manually.

Design options are displayed via an intuitive web-based interface, featuring a 3D model alongside an analytics dashboard with key performance indicators (KPIs) such as cost, route length, land take, and cut-and-fill volumes.

The system can also be used to assess the environmental impact of proposed designs, including carbon footprint, viewshed, noise, and which buildings or areas might be affected.

Based on this information engineers can quickly compare and evaluate multiple design alternatives, then use the software to refine designs further. As the software is cloud based, this makes it easier for multiple stakeholders to understand the consequences quicker, explains Kjølseth

“The typical project manager often has limited access to advanced CAD, BIM or analysis software. With Infraspace they can quickly log into their projects in their browser and see the 3D models together with the analytics instantly,” he says. “It’s also possible to invite external stakeholders into the project to explore a selected number of alternatives.”

Project seeds

To start a project, users can pull in data from various sources, such as Mapbox or Google, or upload custom digital terrain models, bedrock surface models, or GIS data.

The design can then be kickstarted in several ways. An engineer could simply define the start and end point of an alignment, then let the software work out the best alternatives based on set goals. Alternatively, an engineer can define geometric constraints—such as sketching a corridor or marking environmentally protected areas as off-limits.

Users can define where they want the generative AI engine to explore alternatives and define the outcomes, such as, ‘I want options with the least possible construction costs, shortest travel time or length, and the least land take in certain areas’ – Andreas Bjune Kjølseth, CEO, Infraspace

The system is not limited to blank slate designs. It can also import alignments from traditional infrastructure design tools like AutoCAD Civil 3D and use them as a basis for optimisation. As Kjølseth explains, some engineers are even just using the platform for its analytical capabilities, to get fast feedback on traditionally crafted designs. The software offers import / export for a range of formats including LandXML, IFC, OBJ, BCF, glTF, DXF and others.

Adaptability across geographies

Infraspace is not hard coded for specific national design standards, but as Kjølseth explains, the platform captures the fundamental mechanisms of infrastructure design. It allows engineers to define geometric constraints, set curve radii, specify vertical alignment parameters, and adapt to different project types including roads, railways, and power transmission lines. It can handle projects with varying levels of design freedom, from short access roads to expansive highway corridors.

Designed by engineers, for engineers

For civil engineers seeking to streamline their design process, reduce environmental impact, and explore more design options, faster, Infraspace offers an interesting alternative to traditional fragmented workflows. Most importantly, with a team combining civil engineering expertise and software development skills, it’s clear the company understands the nuances of infrastructure design.

While Infraspace is currently focused on early-stage design and optimisation, its ambitions extend beyond. “We will continue to add more features as we go,” says Kjølseth. “I see that generative design as a concept and the platform we have, can definitely be applied to many use cases — during the latter stages of a project, and to even more complex problems.”

Main image: The generative AI engine can deliver thousands of design options in minutes

The post Infraspace: reimagining civil infrastructure design appeared first on AEC Magazine.