DISCRETE EVENT SIMULATION · AGENT EVALUATION

Discrete Event Simulation for Agent Evaluation

Discrete Event 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 systems as state changes triggered by events, queues and resource availability. 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

event timing
queue length
resource utilization
cycle time

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 discrete event simulation for agent evaluation?

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

What should this simulation measure?

event timing, queue length, resource utilization, cycle time.

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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