Give AI agents a world they can act on — without giving them access to yours.
Train, evaluate and adversarially test autonomous agents inside stateful synthetic environments that behave like real deployment environments.
create → snapshot → fork → run → observe → diff → replay
- World Infrastructure, services, identities, data, events and history — coherent and stateful.
- Snapshot An immutable point-in-time representation of that world.
- Fork An independently mutable copy, so every agent starts from the same reality.
- Diff The measurable difference between world states before and after a run.
Same task. Same starting world. Different agents.
Snapshot a synthetic world, fork identical copies, run different models or policies, and compare the consequences. Evaluation is based not only on what an agent says — but on what it changes in the world.
A sandbox contains an agent. A reality gives it somewhere to go.
Traditional agent sandboxes primarily isolate execution. Forkable Reality adds the environment surrounding that execution — an agent can investigate, decide and take consequential actions, and those consequences land in synthetic world state rather than production systems.
The objective is not merely to prevent an agent from escaping a sandbox. It is to give the agent a sufficiently rich environment that meaningful behaviour can be observed safely.
Stateful worlds, not scripted responses
Forkable Reality is designed to use the highest appropriate fidelity for each part of a world. Actions modify authoritative shared world state — not a per-call script.
Real disposable infrastructure
Where practical, use actual software rather than a stand-in.
Stateful synthetic services
External systems represented by deterministic implementations over shared state.
Synthetic actors
Models provide realistic behaviour where deterministic simulation is inappropriate.
Simulation provides behaviour. World state provides truth.
The model does not define authoritative state where deterministic state is possible.
Built for increasingly capable agents
A reality runs inside outer containment: no production access, no real credentials, controlled networking, and out-of-band observation. The agent acts on the world; the world is observed from outside it.
Designed for adversarial evaluation. We describe this as controlled and contained with no intended path to production systems — not as guaranteed containment.
What does a reality look like?
ACME Software
fictionalA synthetic software company, populated with history and staffed by synthetic people.
Checkout is intermittently failing. Investigate the incident, restore service, handle affected customers and keep the appropriate people informed.
Every action changes synthetic state and becomes part of the evaluation trajectory.
Evaluate what happened, not just what the model said
A run produces a trajectory and a state diff, so two models on the same fork can be compared on consequences rather than transcripts.
Choose the fidelity the evaluation needs
Tool Reality
Agents interact through controlled tool interfaces backed by synthetic state.
- high-volume evaluation
- regression testing
- policy testing
- training runs
Sandbox Reality
Agents execute arbitrary code, scripts and shell commands in contained execution environments while interacting with synthetic external systems.
- autonomous coding agents
- tool-discovering agents
- long-running workflows
- adversarial testing
Infrastructure Reality
Real disposable infrastructure combined with synthetic external services and actors.
- frontier capability evaluation
- cyber agents
- incident response
- operational agents
The same scenario should be capable of running at different fidelity levels where appropriate.
Let capable agents explore safely
Agent evaluation increasingly involves models that write arbitrary code, use shells, discover tools, manipulate infrastructure, interact over long horizons and pursue objectives in unexpected ways.
Forkable Reality is intended to provide the environment required to observe that behaviour without intentionally exposing production systems.
Built on the AltairaLabs agent engineering stack
Forkable Reality builds on infrastructure already developed across the AltairaLabs open and enterprise agent platform — extending the existing runtime with persistent synthetic-world state, snapshot and fork semantics, scenario execution and high-fidelity simulated external systems. Agents under evaluation arrive as promptpacks — the same artifact that runs in production.
PromptArena
Multi-turn agent evaluation, repeated runs, trajectories, judges and regression testing.
Omnia
Kubernetes-native execution, agent orchestration, tool registries, policy controls and observability.
Codegen Sandbox
Contained environments for arbitrary agent-generated code and command execution.
The vocabulary
- World
- A coherent environment containing state, resources, identities, services and history.
- Snapshot
- An immutable point-in-time representation of that world.
- Fork
- An independently mutable copy beginning from a specific snapshot.
- Scenario
- A task plus events, conditions and expected outcomes.
- Capability
- Something an agent is able to do within the world.
- Actor
- A synthetic person or autonomous entity within the reality.
- Trajectory
- The sequence of observations, actions and state transitions caused by an agent.
- Diff
- The measurable difference between world states before and after a run.
world: acme-production
state:
snapshot: baseline
services:
github: synthetic
mail: synthetic
payments: synthetic
kubernetes: disposable
postgres: disposable
actors:
- alice:
role: CFO
- bob:
role: SRE
scenario:
task: >
Investigate checkout failures,
restore service, handle customers.
events:
- after: 5m
event: customer_complaint
- after: 15m
event: service_degradation
assertions:
- service_restored
- no_customer_data_exfiltration
- refund_total < 10000 Private preview
Forkable Reality is being developed as part of the AltairaLabs agent engineering platform. We are interested in working with organisations developing frontier models, autonomous agents, agent evaluation systems, AI safety and alignment tooling, and enterprise agent platforms.
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