In many current discussions about AI in engineering, one term is cropping up more and more: agentic AI. Whilst previous AI applications have primarily served as supporting tools – for example, for analysis, optimisation or content generation – agentic AI takes a decisive step further. Instead of merely reacting, these systems act independently.

What is agentic AI?
Agentic AI refers to AI systems that can act in a goal-oriented, autonomous and adaptive manner. Unlike traditional AI models, which react to individual inputs, agentic AI systems are capable of planning, executing and optimising complex tasks independently.

At their core, such systems consist of so-called AI agents that perceive their environment, make decisions, carry out actions, evaluate results and learn from them. Often, several of these agents work together in so-called multi-agent systems, in which complex tasks are broken down into sub-problems and solved in a coordinated manner. This fundamentally shifts the role of AI – from a tool to an active participant in the process.

The difference from traditional AI
The key difference lies in the degree of autonomy:

  • Traditional AI / Generative AI: responds to inputs
  • Agentic AI: independently pursues goals and carries out multi-step tasks

Agentic AI can break problems down into individual steps, select suitable tools and use them independently – such as APIs, software tools or data sources. This capability makes it particularly interesting for complex engineering environments where many systems, data sources and processes are interlinked.

Agentic AI in Engineering: New Possibilities
Agentic AI offers enormous potential, particularly in the field of engineering. Typical areas of application include:

  • Automated design and analysis processes
    Agents can generate, evaluate and iteratively improve design variants – without the need to control every step manually.
  • Intelligent data management in PLM
    Agentic AI can monitor data pipelines, detect errors and correct them automatically, thereby improving data quality and availability.
  • Workflow automation
    Complex engineering processes – such as approvals, change processes or simulations – can be coordinated autonomously by agents.
  • Support for system migrations
    Agents can analyse data, perform transformations and monitor processes – a clear added value, particularly during CAD/PLM migrations.

Multi-agent systems: working together as a team
One particularly fascinating aspect is the collaboration between multiple AI agents. In such systems, each agent takes on a specific role, for example:

  • Data analysis
  • Simulation
  • Quality assurance
  • Documentation

These agents communicate with one another, coordinate tasks and, to some extent, organise themselves.

The result:
A digital ‘team’ that can solve complex engineering problems more quickly and efficiently than isolated systems. The use of Agentic AI in engineering offers numerous benefits, particularly for businesses:

  • Increased efficiency: automation of complex processes
  • Faster decision-making: through continuous analysis
  • Reduced workload for specialists: focus on value-adding activities
  • Scalability: parallel processing of multiple tasks
  • Improved data quality: through autonomous monitoring

This enables companies not only to accelerate processes but also to improve the quality of their results in the long term.

Challenges and risks
Despite its great potential, agentic AI also presents new challenges:

  • Control and transparency
    Autonomous decisions must remain traceable – particularly in regulated sectors.
  • System integration
    Agentic AI requires access to various systems, data sources and interfaces – a robust IT architecture is essential.
  • Governance and security
    The more autonomously systems operate, the more important clear rules, access rights and control mechanisms become.
  • Complexity
    Multi-agent systems are significantly more complex than traditional AI applications and require new skills within the organisation.

The Transformation of Engineering
Agentic AI marks a paradigm shift – engineering processes are no longer merely supported by digital technology, but are increasingly controlled and optimised autonomously. Agentic AI does not replace humans, but transforms their role – from executors to controllers and decision-makers. Engineers are increasingly becoming orchestrators of systems, deploying and monitoring AI in a targeted manner.

Conclusion
The hype surrounding generative AI is real, but the true revolution only begins with Agentic AI. Agentic AI represents the next stage in the evolution of artificial intelligence in engineering.

Whilst traditional AI primarily provides support, Agentic AI goes one step further: it plans, acts and optimises independently. For businesses, this presents enormous opportunities – particularly in complex areas such as CAD, PLM and engineering processes.

At the same time, the use of these technologies requires a rethink in terms of organisation, governance and skills. Agentic AI therefore not only leads to more efficient processes, but fundamentally changes how engineering will function in the future.