SYSTEM SIMULATION · AGENT EVALUATION

System Simulation for Agent Evaluation

System Simulation for Agent Evaluation: model agent policies, consistency, tool use and adaptation across repeatable situations, compare scenarios, and inspect persistent state and autonomous-agent behavior in Epic Simulation.

How this scenario works

Represent connected components, state changes, constraints and feedback over time. For agent evaluation, the model focuses on agent policies, consistency, tool use and adaptation across repeatable situations.

Start with explicit assumptions, change one or more conditions, preserve state history, and compare why trajectories diverge.

What to observe

system state
dependencies
feedback
failure propagation

Also inspect agent choices, resource changes, constraints, feedback loops and second-order effects.

Scenario workflow

Define the system boundary and initial state.
Choose actors, resources, rules and constraints.
Run a baseline before changing assumptions.
Apply one or more interventions or shocks.
Compare state, behavior and outcome differences.
Trace important outcomes back to stored events and assumptions.

A simulation is a model of possibilities, not a guarantee or proof of a real-world outcome.

Questions to test

Which assumptions materially change the result?
Where do bottlenecks, conflicts or unexpected behaviors emerge?
How do autonomous agents adapt as conditions change?
What stored evidence explains the outcome?

FAQ

What is system simulation for agent evaluation?

It applies system simulation to agent policies, consistency, tool use and adaptation across repeatable situations.

What should this simulation measure?

system state, dependencies, feedback, failure propagation.

Does a simulation guarantee a real-world outcome?

No. A simulation explores modeled possibilities under explicit assumptions; it does not prove what will happen in the real world.

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