Exposure
Trace direct and indirect dependencies across the modelled system.
The agentic AI evaluation company
System-level evaluation for agents operating inside complex, adaptive environments.
00 Why representative worlds
Software needs test
environmentsMachine learning needs
representative dataAgentic AI needs
representative worlds
Reality Loops evaluates what emerges when autonomous decisions interact, propagate and alter the environment around them.
ObserveReasonActConsequence
01 Representative worlds
The agent is only one part of the test.
02 Systemic risk
For Investment, Portfolio & Supply Chain Leaders
Decisions travel further than organisations can see.
Model the dependencies, incentives and adaptive behaviours connecting a portfolio, supply network or autonomous infrastructure. Introduce change, then see where exposure accumulates and what contains it.
Map exposure
See where exposure actually sits.
Map the suppliers, facilities and markets connecting a position across jurisdictions.Propagation
See how decisions, shocks and adaptations move through the network.
Stress
Run the system under scarcity, disruption and policy change.
Intervention
Test whether action contains risk, shifts it or amplifies it.
03 Evidence
Evidence at system scale.
Every decision, action, transaction and system outcome is captured at event level. Analysis surfaces behavioural trends, tests model viability and compares system performance across scenarios.
Simulations
Events captured
Since March 2026
Our team brings experience from
Research
Selected research on agent behaviour and systemic risk.
04 Start a conversation
Test agents.
Understand systems.
Evaluate autonomous agents, trace systemic exposure and test how complex environments respond to change.
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