What is Agentic AI? – What Does It Mean for Engineering and Manufacturing?

Agentic AI is artificial intelligence designed to do more than answer questions or generate content.

Instead of requiring a person to specify every individual step, an agentic AI system can be given a goal and then determine how to pursue it. Depending on the system and the permissions it has been given, it may plan a sequence of actions, retrieve information, use software tools, evaluate intermediate results, correct its approach and continue working until it reaches an acceptable outcome.

That is the important distinction.

A generative AI assistant might tell an engineer how to perform a task.

An AI agent may be able to perform portions of that task using the engineer’s software and data.

NVIDIA, for example, describes agentic AI systems as using autonomous, long-running agents to perform complex multi-step work involving research, decisions and actions without requiring human input at every step. Red Hat similarly distinguishes agentic AI from generative AI by describing generative AI as primarily reactive to prompts, while agentic systems pursue goals and interact with tools and data more independently.

For engineering and manufacturing, the change from AI that answers to AI that acts may be one of the most important developments in engineering software.

What Does “Agentic” Mean?

The word comes from agency: the ability to act.

That does not mean an AI system has human intentions, consciousness or free will.

It means the software has been given some degree of freedom to determine what actions are necessary to accomplish a defined objective.

An agentic system may be able to determine:

• what information it needs;
• which tools it should use;
• which steps should happen first;
• whether an intermediate result is acceptable;
• whether it should retry something;
• when another agent or software application is needed;
• when the task is complete;
• when a human needs to intervene.

The level of independence can vary enormously.

One agent may simply diagnose a failed simulation setup and recommend corrective steps.

Another may be permitted to modify the setup, rerun the analysis and examine the result automatically.

Both can be considered agentic, but they clearly have very different levels of authority.

Generative AI vs. Agentic AI

The easiest way to understand agentic AI is to compare it with the generative AI many people already use.

Generative AI

Generative AI creates new content in response to a prompt.

That content might include:

• text;
• computer code;
• images;
• engineering requirements;
• summaries;
• design ideas;
• test cases.

The basic interaction is usually:

Prompt → response

You ask for something.

The AI produces it.

Agentic AI

Agentic AI may use generative AI, but generation is only one part of the process.

An agentic system can potentially:

• plan;
• search;
• retrieve data;
• call software;
• execute calculations;
• run simulation;
• inspect results;
• make choices;
• revise its approach;
• communicate with other agents;
• continue until the assigned objective is achieved.

The process is closer to:

Goal → plan → action → evaluation → adjustment → result

That is why generative AI and agentic AI should not be treated as competing technologies.

Generative AI can be one component inside an agentic system.

What Is an AI Agent?

An AI agent is a software system designed to pursue a goal and take actions toward accomplishing it.

That makes the terminology slightly confusing:

Agentic AI describes the broader approach.

An AI agent is an implementation of that approach.

A company might build one agent to investigate manufacturing defects and another to help configure simulation.

Or it might build a collection of specialized agents that work together.

For example:

• a requirements agent examines product requirements;
• a CAD agent modifies geometry;
• a simulation agent evaluates the design;
• a manufacturing agent checks producibility;
• a sourcing agent examines component availability;
• a compliance agent checks applicable rules.

Those agents might then be coordinated by an orchestration system.

AI Assistant vs. AI Agent

This distinction may be even more useful for engineers.

An AI assistant primarily helps a person perform work.

An AI agent can potentially be delegated part of the work itself.

Imagine an engineer has a simulation that will not mesh correctly.

An AI assistant might explain:

The failure may be caused by poor geometry quality or excessive element distortion. Try these settings.

The engineer then changes the model manually.

An AI agent could potentially:

1. inspect the failed mesh;
2. identify the problematic geometry;
3. determine an appropriate remediation;
4. change the meshing settings;
5. regenerate the mesh;
6. examine whether the failure was resolved;
7. repeat the process if necessary;
8. present the corrected setup to the engineer.

That is no longer merely advice.

It is a task being performed.

We Are Already Seeing This in Engineering Software

Agentic engineering is no longer only a research idea.

Several major engineering-software companies are beginning to introduce agents into commercial or pre-commercial workflows.

Ansys Mesh Agent

With Ansys 2026 R1, Synopsys introduced Mesh Agent within Ansys Mechanical for exploratory use.

The agent is designed to help diagnose and resolve meshing failures during simulation preprocessing.

Instead of merely providing generic documentation about why meshes fail, the agent can use context from the actual engineering problem to guide remediation based on validated approaches.

That is a good example of relatively constrained agentic AI.

The agent is not redesigning an aircraft.

It is tackling a specific, time-consuming engineering problem inside a defined simulation workflow.

Ansys Discovery Validation Agent

Synopsys is also evaluating a Discovery Validation Agent in Ansys Discovery.

Its purpose is to identify potential setup problems using contextual engineering information and established best practices before those mistakes propagate farther into analysis.

Again, this is a useful illustration of where agentic AI may enter engineering first:

not replacing the engineering decision, but automating repetitive inspection and troubleshooting around that decision.

Synopsys AgentEngineer

At the other end of the autonomy spectrum, Synopsys is developing AgentEngineer, a multi-agent architecture for semiconductor design and verification.

In 2026, Synopsys demonstrated workflows in which specialized agents can coordinate extended design-verification tasks rather than simply perform one isolated operation. The company has also demonstrated an autonomous electronics thermal-analysis workflow built around Ansys Icepak that performs simulation setup, preprocessing and post-processing.

That is much closer to the long-term promise of agentic engineering:

an engineer defines the goal and constraints while agents execute significant portions of the workflow.

Siemens Engineering Agents

Siemens is pursuing a similar direction.

Its Eigen Engineering Agent can perform multi-step industrial automation tasks inside TIA Portal rather than merely answering automation questions.

Siemens is also developing specialized engineering and EDA agents that can use traditional engineering software to validate AI-generated decisions.

These are useful examples—but they are one part of a broader industry movement rather than the definition of agentic AI itself.

Why Tool Use Matters So Much

One of the defining characteristics of modern AI agents is their ability to use tools.

That is especially important in engineering.

A language model can know a great deal about heat transfer.

That does not make it a CFD solver.

It can understand structural mechanics.

That does not make it an FEA solver.

It can discuss machining.

That does not make it CAM software.

The interesting possibility is an AI agent that understands when to use those tools.

Suppose an engineer asks:

Why is this electronics enclosure overheating?

An agent might potentially:

1. retrieve the CAD geometry;
2. examine material properties;
3. obtain component power data;
4. review measured operating temperatures;
5. launch a thermal simulation;
6. inspect airflow;
7. compare the simulation with measured data;
8. modify vent geometry;
9. rerun the analysis;
10. compare the alternatives.

The agent does not replace the simulation solver.

It orchestrates the work around it.

That distinction is critical.

Engineering Software May Become Software Used by Software

For decades, engineering software has been designed primarily around a human operator.

An engineer:

• opens CAD;
• selects commands;
• configures simulation;
• enters information into PLM;
• creates manufacturing programs;
• examines results;
• moves data between systems.

Agentic AI introduces another possibility:

Engineering software may increasingly be operated by other software.

Instead of telling an application exactly which commands to execute, the engineer might provide:

• the objective;
• the constraints;
• engineering intent;
• acceptance criteria.

An AI agent could then determine which tools and steps are required.

That may ultimately change engineering software interfaces every bit as much as generative AI changes the way we search for information.

What Is a Multi-Agent System?

A single agent does not need to do everything.

A multi-agent system uses several agents, often with different specialties, that cooperate to complete a larger task.

NVIDIA describes multi-agent systems as environments in which specialized agents divide work, share information and perform different tasks in parallel.

An engineering change might therefore involve:

Requirements Agent
Checks whether the proposed change still satisfies requirements.

Design Agent
Modifies geometry.

Simulation Agent
Evaluates structural and thermal behavior.

Manufacturing Agent
Checks whether the new part can be produced.

Supply Agent
Evaluates material or component availability.

Cost Agent
Calculates the economic impact.

A human engineer or engineering manager may then review the combined result.

The individual agents do not necessarily need to understand the entire problem.

They can specialize.

What Is AI Orchestration?

Once many agents become involved, something has to coordinate them.

That is AI orchestration.

An orchestration layer may determine:

• which agent receives a particular task;
• which software tools it can access;
• which enterprise data it may use;
• which agent should work next;
• which steps can run simultaneously;
• what happens if agents disagree;
• which actions require human approval;
• how decisions are logged;
• when the process should stop.

This is why companies are simultaneously developing agents and orchestration platforms.

The agent performs work.

The orchestration layer coordinates and governs the larger environment.

Where Does NVIDIA Fit?

NVIDIA is interesting because it is approaching agentic AI largely as infrastructure.

Rather than trying to own every specialized engineering application, NVIDIA provides models, runtimes and development tools that software vendors can use to create agents.

Its Agent Toolkit includes components for models, tools, agent skills, security controls and runtime infrastructure.

That helps explain why competing engineering vendors can all pursue agentic systems while using some of the same underlying AI infrastructure.

Agentic engineering will probably not belong to one CAD, simulation or PLM company.

It is more likely to become a layer that cuts across the entire engineering software market.

What Could Agentic AI Do in CAD?

Potential uses include:

• finding existing designs;
• interpreting requirements;
• creating preliminary geometry;
• modifying features;
• checking company modeling standards;
• selecting components;
• evaluating design alternatives;
• preparing drawings;
• identifying dependencies before making changes.

A CAD agent might eventually receive an instruction such as:

Reduce the weight of this bracket by 15 percent without reducing the required safety factor or changing the mounting interface.

It could then modify geometry, invoke simulation and iterate through alternatives.

The important part is that the engineer provides the intent and constraints, rather than specifying every modeling operation.

What Could Agentic AI Do in Simulation?

Simulation is already becoming one of the clearest early applications.

An agent might:

• select an analysis type;
• prepare geometry;
• assign materials;
• generate a mesh;
• detect meshing failures;
• establish boundary conditions;
• run the solver;
• examine convergence;
• identify suspicious results;
• refine the model;
• rerun the analysis;
• compare alternatives.

The Mesh Agent and Discovery Validation Agent introduced with Ansys 2026 R1 are early examples of this progression.

Simulation also provides something AI urgently needs:

a way to test whether an idea actually obeys physics.

Physics-Based Software Could Become the Agent’s Reality Check

Generative AI operates probabilistically.

Engineering physics does not.

A proposed bracket either survives the required load or it does not.

A cooling system either keeps components within their allowable temperature range or it does not.

A circuit either satisfies electrical constraints or it does not.

That makes traditional engineering software exceptionally valuable in an agentic environment.

Rather than trusting an AI model’s verbal reasoning, an agent can call:

• CFD;
• FEA;
• electromagnetic simulation;
• circuit simulation;
• motion analysis;
• tolerance analysis;
• manufacturing simulation.

The AI proposes.

The engineering tool tests.

The AI evaluates the result.

The engineer retains responsibility for deciding whether the assumptions and conclusions are valid.

This combination may be considerably more important than simply attaching a chatbot to engineering software.

What Could Agentic AI Do in Manufacturing?

Manufacturing gives agents access to a different set of tools and information.

Potential applications include:

• investigating quality problems;
• identifying recurring production anomalies;
• analyzing machine data;
• optimizing schedules;
• troubleshooting automation;
• helping create CNC programs;
• evaluating production changes;
• identifying maintenance needs;
• correlating supplier or material information with defects.

A manufacturing agent might receive:

Determine why scrap increased on this production line during the last two weeks.

Instead of returning a generic list of possible causes, the agent could potentially examine production data, quality records, machine histories, material batches and maintenance records before recommending the most likely explanation.

What Could Agentic AI Do in PLM?

PLM may become particularly important because AI agents need trustworthy product context.

An agent could potentially use systems such as Teamcenter, Windchill, ENOVIA, Aras or other PLM platforms to:

• identify the correct revision;
• determine which configurations use a component;
• find related requirements;
• inspect engineering-change history;
• locate approved alternatives;
• identify affected documentation;
• assemble change packages;
• route information through controlled processes.

This is one of the places where agentic AI becomes more than a clever assistant.

An AI system can produce a technically correct answer about the wrong product revision.

PLM context can help prevent that.

What Could Agentic AI Do in EDA?

Electronic design automation is another area where agentic systems are moving quickly.

Synopsys has demonstrated AgentEngineer-based multi-agent workflows spanning chip design and verification, including long-running verification tasks that normally require repeated engineering intervention.

This is particularly interesting because semiconductor design already involves enormous chains of specialized tools.

An agent does not need to replace those tools.

It can coordinate them.

EDA may therefore become one of the earliest engineering fields where multi-agent workflows become normal.

Does Agentic AI Mean Autonomous Engineering?

Not necessarily.

Agentic and fully autonomous are not synonyms.

Agentic systems can operate at different levels of independence.

At one end:

AI identifies a problem and suggests an action.

Farther along:

AI makes the change but waits for an engineer to approve it.

Farther still:

AI performs an entire routine workflow automatically and alerts an engineer only when something falls outside acceptable limits.

At the extreme:

AI performs a long-running engineering process with little human intervention.

All of those can involve agents.

The appropriate level depends on risk.

Human-in-the-Loop vs. Human-on-the-Loop

These terms become important as agents gain more authority.

Human-in-the-loop generally means a person must participate in or approve important stages.

For example:

Agent proposes engineering change → engineer approves → workflow continues

Human-on-the-loop generally means the agent performs more of the work independently while a person supervises the process and intervenes when necessary.

For example:

Agent runs standard production analyses → engineer reviews exceptions

Engineering organizations will likely use both approaches.

The appropriate degree of human involvement should depend on the consequences of an error.

Generating a routine report and modifying safety-critical flight software clearly should not receive identical levels of autonomy.

Is Agentic AI Just Automation With a New Name?

No—but there is overlap.

Traditional automation follows workflows humans explicitly define.

For example:

If pressure exceeds X, shut the valve.

The program already knows exactly what to do.

Agentic AI introduces more flexibility.

An agent might receive:

Determine why pressure is exceeding the expected operating range and identify the appropriate corrective action.

It then determines how to investigate.

A useful distinction is:

Traditional automation follows a predefined path.

Agentic AI can determine the path toward a predefined goal.

That flexibility is the source of both its promise and its risk.

Is Agentic AI Really New?

Not entirely.

Software agents, expert systems, planning systems and autonomous control have existed for decades.

What has changed is the capability of modern AI models to:

• understand natural language;
• reason across unstructured information;
• choose among available tools;
• interpret results;
• communicate with people;
• coordinate with other agents.

Large language models provide a flexible layer between human intent and existing software.

That makes practical agentic systems much easier to create.

So agentic AI is both a genuinely important technological development and a new label placed on some much older computer-science ideas.

Both things can be true.

What Are the Risks of Agentic AI in Engineering?

Giving AI the ability to act introduces risks beyond those associated with a conventional chatbot.

An Incorrect Answer Can Become an Incorrect Action

A chatbot can give bad advice.

An agent may be capable of implementing it.

That changes the risk substantially.

Errors Can Compound

An agent can make an early mistake and then perform every subsequent step perfectly.

If it selected the wrong product configuration at the beginning, excellent downstream analysis may still produce the wrong conclusion.

Agents Can Be Given Too Much Authority

Access should be limited to what an agent actually needs.

An agent designed to examine simulation results does not automatically need permission to release engineering changes.

Traceability Becomes Essential

Engineering organizations need to know:

• what information an agent used;
• which software it called;
• what actions it took;
• what assumptions were made;
• what changed;
• who approved the outcome.

Cybersecurity Becomes More Complicated

An agent capable of using enterprise tools and credentials creates another avenue through which systems can potentially be misused.

NVIDIA’s own agentic platform emphasizes policy-controlled access to files, networks, credentials and tools, illustrating how central security becomes when AI can act rather than merely respond.

Automation Bias May Be the Quietest Risk

Engineers may eventually become accustomed to approving agent-generated work because it is correct almost all the time.

The danger is that almost all the time is not good enough for every engineering decision.

The better these systems become, the easier it may be for humans to stop looking closely.

Will Agentic AI Replace Engineers?

It is too early to make sweeping predictions, and the current engineering implementations do not support the idea that engineers are about to disappear.

What they do suggest is a change in which parts of engineering work humans perform directly.

The early agentic capabilities being commercialized target tasks such as:

• troubleshooting;
• preprocessing;
• repetitive configuration;
• verification;
• information retrieval;
• workflow coordination;
• repeated iterations.

Those are tasks surrounding engineering judgment.

The more difficult questions remain human questions:

• Are the assumptions valid?
• Is the requirement correct?
• Is this design safe?
• Is this tradeoff acceptable?
• Is the simulation appropriate?
• Should this change be released?

Agentic AI may reduce the amount of mechanical interaction engineers have with software.

That does not necessarily reduce the importance of understanding engineering.

It may increase it.

Why Are Engineering Software Companies So Interested?

Engineering software has become extraordinarily capable—and extraordinarily complex.

Many workflows now require specialists who understand:

• which software to use;
• which settings matter;
• how information moves between applications;
• which solver is appropriate;
• how to troubleshoot failures;
• how to interpret results.

Agents offer vendors a way to hide some of that complexity.

Instead of requiring engineers to understand every command in every application, software could increasingly accept higher-level intent.

The user says:

Determine whether this design meets our thermal requirements.

The agent figures out which tools and processes are required.

That is a fundamentally different interaction model.

The Future May Be Less About “Using Software”

Today we talk about learning CAD, learning simulation or learning PLM.

Agentic AI suggests a future where engineers may spend less time learning every individual software operation and more time defining:

• goals;
• requirements;
• constraints;
• acceptable results;
• engineering intent.

The software increasingly handles the intermediate execution.

That does not eliminate engineering expertise.

Someone still needs to know whether the objective was sensible in the first place.

And someone must recognize when the answer is wrong.

Why Agentic AI Matters

The significance of agentic AI is not that computers suddenly became engineers.

They did not.

The important change is that AI is moving from a system that talks about work toward a system that can increasingly participate in performing work.

In engineering, that means AI agents may eventually operate the same CAD, simulation, PLM, EDA and manufacturing applications engineers use today.

The engineer could move farther away from individual software commands and closer to defining engineering intent and judging results.

Whether that becomes a productivity breakthrough or a dangerous exercise in over-automation will depend largely on how carefully engineering organizations define the boundaries between:

what AI may do,

what engineering software can verify,

and

what humans must ultimately decide.

Frequently Asked Questions about Agentic AI