# Using Agentic AI to do simulation

**URL:** <https://community.opal-rt.com/t/using-agentic-ai-to-do-simulation/176>\
**Category:** Q&A\
**Tags:** discussion, energy, general\
**Created:** [August 25, 2026, 1:28am UTC](https://community.opal-rt.com/t/using-agentic-ai-to-do-simulation/176 "2026-08-25T01:28:03Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![Vincent\_Jiang](https://yyz2.discourse-cdn.com/flex012/user_avatar/community.opal-rt.com/vincent_jiang/32/469_2.png) [@Vincent\_Jiang](https://community.opal-rt.com/u/Vincent_Jiang)\
**Post date:** [August 25, 2026, 1:28am UTC](https://community.opal-rt.com/t/using-agentic-ai-to-do-simulation/176/1 "2026-08-25T01:28:03Z")

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Is it possible for Agentic AI to perform OPAL-RT simulations?

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<div class="post-metadata">

**Author:** ![PFA007](https://yyz2.discourse-cdn.com/flex012/user_avatar/community.opal-rt.com/pfa007/32/120_2.png) [@PFA007](https://community.opal-rt.com/u/PFA007)\
**Post date:** [August 28, 2026, 1:30am UTC](https://community.opal-rt.com/t/using-agentic-ai-to-do-simulation/176/2 "2026-08-28T01:30:13Z")

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Yes—agentic AI can be used to operate and automate OPAL-RT simulations, but it should act as an orchestration and analysis layer rather than replace the deterministic real-time simulator.

A practical architecture would be:

AI agent interprets the objective  
For example: “Run a sweep of load resistance from 5 Ω to 50 Ω and identify the point where the DC-link voltage becomes unstable.”

Deterministic automation layer executes the workflow  
A Python, MATLAB, LabVIEW, C++, or TestStand application can:

Load and compile the RT-LAB model

Start, stop, reset, and monitor execution

Set exposed model signals or runtime parameters

Run test sequences and parameter sweeps

Collect logs, scopes, and measured signals

Generate pass/fail results

RT-LAB documentation describes a comprehensive API for custom online applications and dynamic modification of model signals or parameters during execution. [https://opal-rt.atlassian.net/wiki/spaces/RD/pages/45880058/RT-LAB+User+Guide+Introduction](https://opal-rt.atlassian.net/wiki/spaces/RD/pages/45880058/RT-LAB+User+Guide+Introduction)

The AI agent analyzes the results  
The agent can detect violations, compare results against requirements, identify anomalies, propose the next test, and produce a report.

Safety and authorization gates remain in place  
For HIL or power-HIL systems, the agent should not be allowed to arbitrarily change hardware-facing signals, protection thresholds, or operating conditions without limits and approval. The execution layer should enforce:

Allowed parameter ranges

Rate limits on changes

Emergency-stop handling

Hardware interlocks

Test approval and audit logging

A defined safe state after communication loss