What they never told you about autonomy and the need for synthetic environments

The same way a human develops judgement through repeated exposure to the real-world, autonomous military systems need to be trained through repeated exposure to operational scenarios. This is where high-fidelity synthetic environments become essential.

Imagine using a synthetic environment to train for a mission with a mixed force of autonomous and crewed platforms, operating in contested airspace.

A group of autonomous aircraft supports a strike mission against a heavily defended target. Some platforms perform reconnaissance, whilst others conduct electronic warfare and deploy kinetic effects. Human operators remain responsible for mission intent and authorisation, while autonomous systems manage many of the lower-level decisions required to execute the mission safely and effectively.

As the mission unfolds, image-classification systems identify potential air-defence sites from sensor feeds. Sensor-fusion algorithms combine information from radar, electronic warfare measures and electro-optical sensors to create a coherent picture of the battlespace. Decision-making systems assess the probability of detection, calculate alternative routes and recommend courses of action. Language-based systems communicate recommendations back to operators in a form that can be quickly understood and approved.

None of these decisions are meaningful unless the synthetic environment itself behaves like reality.
The missile system protecting the target must perform like its real-world counterpart. The radar must exhibit realistic detection characteristics. The combat aircraft defending the airspace must employ believable tactics. Electronic warfare effects must accurately reflect how signals propagate, degrade and interact. Munitions must behave as they would in operational service. Weather, terrain and communications must influence outcomes in realistic ways.

This is precisely the level of fidelity we engineer into our own synthetic environments. Our MIMESIS maritime synthetic environment, for example, models terrain, seabed, weather and underwater acoustics, and digitally replicates combat systems, sensors and effectors. In some configurations it goes further still, driving real operational software and using the same physical terminals that operators would use at sea. Project OdySSEy extends that same principle across land, air and space, delivering a full representation of the battlespace with realistic modelling of physics, sensors and weapons.

Without this fidelity, autonomous systems may learn lessons that are entirely wrong.

A route that appears safe in a simplified simulation may be highly vulnerable in reality. A target-recognition model trained against unrealistic sensor data may perform poorly when exposed to operational conditions. A collaborative autonomy algorithm may appear highly effective against simplistic adversary behaviour, but fail when confronted by a capable opponent.

In other words, the value of synthetic experience depends entirely on the quality of the synthetic world generating it. The goal is not simply to create more data. The goal is to create authentic experience.

Every radar contact, missile launch, communications interruption and tactical decision should contribute to an environment that behaves as closely as possible to the real world. Only then can autonomous systems develop the judgement, adaptability and resilience required for operational deployment. It’s why we use OdySSEy not only to rehearse new missions and optimise tactics, but to generate the realistic data needed to train military AI models, exposing systems to conditions that would be dangerous, expensive or impossible to recreate physically.

This is ultimately why synthetic environments are becoming such a strategic capability.

The organisations that can create the most realistic representations of operational reality gain the ability to generate vast quantities of meaningful experience. They can accelerate learning, identify weaknesses and build confidence before a system is ever deployed. A modular, open architecture matters here too. Because OdySSEy can integrate third-party simulations and new capabilities without re-architecting the whole environment, we can test concepts and refine tactics without first building physical prototypes, then evolve the synthetic world as the threat evolves.

The future of autonomy will not be determined solely by the sophistication of the AI. It will be determined by the quality of the world in which that AI learns.