PRAI Control Platform
PRAI Control Platform is a cloud-native governance and execution control layer for autonomous and AI-driven systems. It sits between autonomous decision-making components and real-world execution, so that actions are constrained, observable, and auditable.
What it does
- Execution Control Apply rules that govern what autonomous systems are allowed to execute.
- Runtime Governance Enforce constraints at runtime to prevent out-of-scope or unsafe actions.
- Audit & Traceability Record decisions, actions, and system state in a structured, reviewable form.
Current stage
The platform is in active development with simulation-based demonstrations.
The autonomous drone scenario shown below is one representative test environment used to validate execution control and observability before deployment.
Execution control and observability
This simulation evaluates how PRAI’s execution control layer behaves under repeated, randomized runtime conditions.
*Figure 1: Visualized State Transitions during randomized runtime conditions.
Execution Governance Testing in a Simulated Autonomous Drone Environment
PRAI uses simulated autonomous drone environments as a representative real-world scenario to test how execution control and observability behave under uncertainty.
Autonomous aerial systems operate in dynamic conditions, execute actions in real time, and are sensitive to constraint violations. This makes them a useful test case for validating execution governance before deployment.
Why Autonomous Drones
The purpose of this simulation is not to test autonomy itself, but to observe how execution is governed, constrained, and recorded under repeated variation.
How the Monte Carlo Simulation Works
The simulation runs the same autonomous scenario repeatedly using Monte Carlo methods, introducing randomized variables across each run. Each iteration allows PRAI to observe how its control and audit layer responds to different execution paths and environmental conditions.
Simulation Inputs
- › Thousands of repeated simulation runs
- › Randomized environmental parameters
- › Variable execution paths
- › Defined execution constraints and control rules
- › Runtime event logging enabled
Observed Outputs
Across simulation runs, PRAI records:
- › Execution decisions taken per run
- › Constraint enforcement events
- › Blocked or altered execution paths
- › Runtime state transitions
- › Structured audit and event records
What Is Measured
The simulation focuses on operational characteristics such as execution path variation, constraint violation attempts, runtime response consistency, completeness of audit records, and successful completion of simulation runs.
No guarantees or outcomes are assumed — the purpose is to observe system behavior, not to prove correctness.
How This Is Used
These simulations are used internally to validate execution governance and observability before systems are deployed into production or edge environments. They allow PRAI to identify weaknesses, refine control rules, and improve runtime visibility under controlled conditions.
Representative Scenario
The autonomous drone environment shown here is one representative test case. The same simulation and governance approach can be applied to other autonomous or automated systems that require controlled execution and auditability.
Summary: Monte Carlo simulation provides PRAI with a repeatable, cloud-native way to stress-test execution control in realistic conditions, ensuring systems behave predictably and remain observable as complexity increases.