Reflexion OS
An operating layer between AI intelligence and physical machines.
Reflexion OS gives intelligent agents a common way to work with robot capabilities while keeping robot-specific execution below the task layer.
The system
The system connects reasoning, persistent memory, perception, capabilities, execution feedback and independent physical control.
Work through capabilities
The Brain reasons about semantic actions rather than robot-specific motor interfaces.
Capabilities connect task intent to the implementation available for a supported robot.
inspect(area)navigate(location)grasp(object)place(object, destination)Any brain, common harness
Use the intelligence that fits the task.
Reflexion OS is not tied to one AI model.
A frontier model, a local model or a specialized agent can reason through the same operating layer. The robot application does not have to be rebuilt around each generation of AI.
Better models become better Brains inside the system.
inspect()grasp()place()The Brain changes. The task and the capabilities stay the same.
Different ways to execute the same capability
Keep the task, change what runs underneath.
A capability is not tied to a single implementation.
Depending on the robot and the task, Reflexion can connect task-level intent to classical robotics, manufacturer skills, learned policies, vision-language-action models or specialized controllers.
The Brain reasons about what needs to happen. The capability handles how it happens on that machine.
grasp(component)
Runs on
Classical control
Across robots
One operating layer across robots.
Robot-specific integration should stay below the task.
Reflexion separates what the application wants from how each supported robot performs it. Once a platform is integrated, its capabilities can become reusable building blocks for new applications instead of starting again from the robot SDK.
Change the robot. Keep working at the task level.
grasp(component) place(component, tray)
RobotCollaborative arm
Use different forms of intelligence
Reflexion can work with different reasoning models and different capability implementations.
Classical robotics, learned policies, OEM skills and specialized AI can contribute to the same application at different levels.
Observe and adapt
Execution and observation continue together.
The Brain can use new evidence to continue, inspect, cancel or replan instead of waiting blindly for a long action to finish.
Move the component to the assembly tray.
Hierarchical by design
Reason at the task level, control at the robot level.
Intelligence and physical control do not run at the same pace.
The Brain chooses goals, strategies and capabilities. Robot skills and policies execute locally at the cadence required by the physical task.
This lets advanced AI reason over the mission without turning a language model into a motor controller.
When the task changes, the Brain can reason and adapt.
When physical execution requires an immediate response, local control does not wait for the Brain.
Memory
Remember what matters.
A useful agent needs more than the latest prompt.
Reflexion maintains context across decisions and longer-running tasks: the objective, relevant objects and places, what has already happened and the execution history needed for the next decision.
Memory gives the agent continuity instead of forcing every action to start from zero.
- 1Component A placed in tray 1.
- 2Component B placed in tray 2.
- ||Task interrupted: the cell is needed for another job.
- …Two hours later
- ▶Task resumed: component C, tray 3.
- Objective Kit three components into the assembly trays.
- Done A and B placed and observed.
- Objects & places Component C last seen on the bench, left. Tray 3 moved to the conveyor end.
- Next
grasp(component_c)
Skills can grow
When the capability is missing.
An operating system for physical AI cannot remain a fixed library of behaviors.
Through Skill Foundry, we develop agents that can use execution experience to diagnose capability gaps and decide whether to reuse an existing skill, compose available capabilities, generate code or train a new behavior.
The capability system can grow with the tasks it encounters.
insert(memory_module)
- Existing capabilityIs there one that already does it?
- ComposeCan available capabilities be combined?
- CodeCan the behavior be written?
- TrainDoes it need to be learned?
- New candidate capabilityReady for independent evaluation.
Where Skill Foundry meets safety
Learning can create capabilities without controlling deployment.
Increasing autonomy should not give the learning process increasing physical authority.
Reflexion separates capability development from the authority to put a behavior onto a robot. New and updated behaviors remain subject to an independent execution path before they can control physical systems.
Govern physical execution
Intelligent intent and physical authority remain separate.
Reflexion controls the boundary between what the AI requests and what is allowed to reach the robot.
Place the component.
More capable intelligence does not automatically receive more physical authority.
Observability
Know what actually happened.
Physical AI needs more than an agent saying that a task succeeded.
Reflexion connects the agent’s decisions with execution evidence: what was requested, what was admitted, what ran and what the robot observed. This makes tasks easier to debug, evaluate and improve.
inspect(workspace)AdmittedWorkspace inspectionObject located.grasp(component)AdmittedSuction graspGrasp observed.move_to(target)Not admitted · outside the allowed workspace——place(component, tray)AdmittedPlacementPlacement observed.The same operating problem extends beyond robots.
Software agents and physical agents both need persistent context, callable capabilities, execution feedback and recovery.
Reflexion applies these system principles across digital and physical environments. Physical AI adds embodiment, dynamics and independent control of real-world execution.
Build a pilot with Reflexion
Tell us about the task and the equipment you have. We work with robot owners, industrial teams, integrators and manufacturers to prototype the application, connect the required capabilities and evaluate the path to deployment.