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Agentic AI toolkit
AI agents are beginning to operate engineering software autonomously. Engineering teams that put AI agents to work on simulation tasks complete more design iterations, evaluate more candidates, and reach better decisions faster - without adding headcount.
The question is not whether this will affect simulation-driven engineering - it is whether your tools are ready to be called by an agent. pSeven Enterprise is built to make that possible. Your simulation expertise, already captured in workflows and apps, becomes directly accessible to AI agents through standardized interfaces - without rebuilding or rewrapping anything.
What does agentic AI mean
for engineering automation
AI agents are LLM-based software systems that can plan, decide, and act across multiple steps to reach a goal – calling tools, reading results, and adjusting their approach along the way without human direction. In engineering, this means an agent can be given a high-level objective and then autonomously navigate the software, data, and decisions needed to get there. Rather than executing a single instruction, an agent reasons through a sequence of steps: for example, performing an optimization task to determine the most suitable fasteners and torque magnitude for a given part, working within the standard bolts and nuts available in the organization's inventory.
Engineering organizations are clear about what they expect from this shift. CIMdata's first global independent study of AI in PLM, conducted in early 2026, surveyed industrial companies, software providers, and service providers to understand where AI is actually delivering value across the product lifecycle. Despite the differences in industry and maturity, the priorities on the customer side are consistent:
looks_one Speed
Reduced design and development cycle time and faster access to knowledge.
looks_two Efficiency
Reduced manual or repetitive tasks and improved workflow efficiency.
looks_3 Innovation
Increased design exploration and broader evaluation of alternatives.
The gap between these expectations and practical reality comes down to one question: how does the agent reach the tools it needs? Simulation software does not come with a chat interface. Solvers do not respond to natural language. Until an agent can reliably call, monitor, and chain engineering tools, this capability remains confined to demonstrations rather than real workflows.
New to AI in engineering automation?
Read our article "ABCs of AI in engineering automation" that is specifically created to help people who are just beginning to explore AI technologies in engineering, or who do not yet have a thorough understanding of them, to better understand their scope of application, limitations and prospects.
What AI agents can do
in pSeven Enterprise
Agentic AI toolkit in pSeven Enterprise sits at the point where engineering expertise meets formalization - helping capture simulation processes into workflows, and then exposing them through apps to users and AI agents. Rather than requiring teams to rebuild that expertise in a new form for AI consumption, pSeven Enterprise exposes it through MCP, a standardized protocol that any compatible AI agent can connect to. The result is a direct bridge between the agent and your simulation environment: the agent discovers what tools are available, understands their inputs and outputs, and calls them as part of a larger automated task.
Agentic AI toolkit in pSeven Enterprise is organized into three layers. The first makes your published simulation apps callable by an agent through a natural language interface. The second embeds AI reasoning inside workflows themselves, giving them the ability to handle variability without breaking. The third opens workflow authoring and management to the agent directly, enabling fully autonomous engineering cycles. Each layer is independent - you can adopt the first without committing to the others.
Provide deterministic tools to non-deterministic AI agents
AI agents are powerful planners but unreliable executors - they reason well but cannot guarantee numerical precision or physical correctness on their own. pSeven Enterprise gives agents a set of validated, deterministic simulation tools to call, so the parts of the workflow that require exactness are handled by software built for exactly that, while the agent focuses on orchestration and decision-making.
Make simulation accessible to more employees
When simulation expertise is captured in a workflow and published as an app, it becomes callable by an AI agent through a plain language interface - no knowledge of the underlying solver, the parameter space, or the toolchain required. Engineers outside the simulation team, project leads, systems architects, and procurement specialists can run validated analyses by describing what they need, and the agent handles the rest.
Build automation that survives contact with reality
Rigid automation breaks the moment something unexpected happens - a mismatched output format, a missing field, a solver result that needs interpretation before the next step can proceed. Embedding AI reasoning inside workflows gives the automation the judgment to handle variability, flagging issues, and keeping the pipeline moving where a hard-coded script would stop.
Execute and chain simulation apps
via chat interface in natural language
The first layer of the toolkit connects AI agents to your published apps on AppsHub. Once connected, an AI agent operates with the same access rights as the user who established the connection, unless these are intentionally limited. The agent can then browse the available apps, inspect the inputs and outputs required and produced by each app, run the apps with values determined by the agent or received from a previous step, and pass the results forward into the next stage of the pipeline - all driven by natural language instructions from the engineer. Tasks that previously involved manually navigating the interface, configuring inputs, waiting for results and entering them into the next tool can now be delegated to an agent with a single prompt. The engineer stays in control over what is approved and acted upon, while the agent handles the execution mechanics.
Publication of workflows into Appshub as simulation apps and calling them via a chat interface.
Available now
Introduce process tolerance in workflows
with non-rigid automation
The second layer can be embedded within the workflow itself. Real engineering workflows often encounter variability that rigid automation cannot handle, such as unexpected output formats, missing fields, and unit inconsistencies between connected tools. Solver results also require interpretation before the next step can proceed. Agentic-ready blocks embed AI reasoning directly inside workflows, resolving these situations without halting execution or requiring manual intervention. A block with AI can read ambiguous output, infer the correct interpretation, reformat data to match downstream inputs, flag issues with structured diagnoses rather than silent failures or create a custom report. This is the difference between automation that breaks at the edges and automation that can handle them.
Beyond resolving variability, a block with AI can be configured as an autonomous agent with a specific role and its own system prompt – a mechanical specialist, a units converter, a results interpreter, a report writer. Rather than relying on a single agent to handle an entire workflow end to end, responsibilities are distributed across purpose-built agents embedded at the points where their expertise is needed. This is a natural bridge to multi-agent system architecture. It also carries a practical advantage: each agent block operates within its own context window, focused on a well-defined task with only the inputs it needs. A single agent handling too many steps loses track of earlier information and produces degraded output. Decomposing the workflow into specialized agents, each running lean and focused, keeps the overall system reliable regardless of the complexity of the process it automates.
A workflow featuring a specialized AI block for generating reports.
Available on request
Build and execute workflows
autonomously in agentic cycles
The third layer extends agent access to Studio, opening up workflow authoring and management alongside execution. An agent connected to Studio can assemble workflows, configure blocks, execute runs, and publish them as apps in AppsHub - operating in full agentic cycles where each iteration informs the next. This enables scenarios where the agent actively builds and refines the process in response to intermediate results, effectively acting as an engineering automation assistant that works at the workflow level rather than the task level. Combined with the ability to chain AppsHub apps through a chat interface, this gives the engineer a path from open-ended agentic exploration to a fully deterministic, repeatable workflow - capturing what the agent discovered and turning it into a process that runs reliably without agent involvement every time.
Running a sequence of simulation apps, assembling a deterministic workflow and publishing it back into AppsHub.
Available on request
Accelerate adoption of agentic AI
with a variety of agentic-ready blocks
Whether you are starting with AppsHub execution, adding agentic-ready blocks to existing workflows, or exploring Studio-level automation, our Marketplace provides ready-to-use building blocks for each layer. The Agent block, the Agentic App Reference, and the Agentic DSE block are available now and cover the most common entry points for teams getting started.
Agent
LLM agent that calls other workflow blocks as tools. Give it a task on User prompt; it plans, calls the tools you wired, and answers on Response.
Agentic App Reference
References a published AppsHub app and runs it as a block - the agentic analog of the built-in App reference block.
Interested in agentic AI for your engineering workflows?
We are actively developing this capability and working with early adopters. If you want to explore what agentic automation could look like in your simulation environment, let's start the conversation.
Frequently asked questions
The AI technology has crossed a threshold in engineering. LLMs can now reason reliably enough across multi-step technical tasks that agents are moving from research environments into production workflows at early-adopter organizations. The teams building the interfaces between their simulation environments and AI agents today are establishing a capability advantage that will be difficult to close in two or three years. The cost of waiting is not staying still - it is falling behind organizations that are not waiting.
We are making capabilities marked with "Available on request" available to selected early adopters. Participants will be in a direct contact with the product team to better shape the future of this functionality. If your organization is evaluating AI in engineering automation, this is the right moment to be involved.
Any LLM that powers an MCP-compatible desktop AI client can be used with pSeven Enterprise. The MCP protocol is model-agnostic - pSeven Enterprise does not communicate with the LLM directly, only with the client application that wraps it. This gives your organization the flexibility to use the AI provider that meets your performance, cost, data residency, and compliance requirements, and to switch providers without any changes on the pSeven Enterprise side.
We actively use the following models or better in our tests and can recommend them:
- MCP for Appshub: GLM-4.7.
- MCP for Studio: GLM-5V-Turbo, Kimi K2.6, DeepSeek-V4, Qwen3.6-Plus, GPT‑5.3, Sonnet 4.6, Opus 4.8.
The MCP server exposes your app and workflow interfaces to the AI agent - inputs, outputs, and run status. What data is transmitted to the AI provider depends on what the agent sends as part of its reasoning process, which is determined by your AI client configuration and the AI provider's data handling policy. pSeven Enterprise itself does not send simulation data to any external AI service. We recommend reviewing your AI provider's data residency and privacy terms for enterprise deployments.
Yes - and this is one of the core design principles behind pSeven Enterprise. The platform has always been built around connecting simulation environments rather than replacing them, so existing tool integration is the starting point rather than an afterthought.
At the execution level, the platform's built-in resource manager handles concurrent simulation runs across your server infrastructure, so adding more agents driving more workflows does not require a separate scaling strategy - it uses the same execution capacity you already manage.
A working prototype that connects an LLM to a simulation tool through a chat interface can be assembled in days. The challenge is everything that follows: concurrent users, access rights control, run state management, failure recovery, auditability, etc. Vibe-coded solutions are fast to start and expensive to maintain - the ongoing cost of maintaining a custom integration typically far exceeds the cost of building it, plus it pulls engineering talent away from the work it was hired to do.
pSeven Enterprise provides that infrastructure as a maintained, versioned platform. Your team's effort goes into the workflows, apps, and agent configurations specific to your engineering processes - where your competitive differentiation actually lives.