Agent Simulation
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Agent Simulation is the practice of converting production observability traces from deployed AI agents into controlled, repeatable offline experiments. Rather than relying solely on live production runs, it reconstructs tasks from real traces to enable systematic evaluation and improvement of agent behavior.
Core Concept and Motivation
Rustem Feyzkhanov (Snorkel AI) advocates for agent simulation as a direct alternative to purely observing production traces. The key insight is that raw Observability Traces|observability traces are useful for debugging but not for rigorous comparison: offline simulation "turns traces into repeatable experiments. Now you take production traces, you construct tasks," enabling apples-to-apples comparison across agent versions. 4:00
This approach expands the measurement surface beyond simple pass/fail rates to include cost, latency, and retries — metrics that matter for production systems but are difficult to isolate from noisy live traffic. 4:00
Use Cases
Fine-Tuning Smaller Models
Simulation environments serve as a substrate for model distillation. Feyzkhanov reports that simulation was used to "fine-tune small plan model to match performance of large plan model" on specific tasks 5:54 — using the controlled replay environment to generate training signal that would be prohibitively expensive or slow to gather in production.
Evaluation and evals|Evaluation Infrastructure
By constructing tasks from production traces, simulation environments function as a form of evals: structured benchmarks grounded in real usage rather than hand-crafted test cases. This grounds evaluation in the actual distribution of tasks agents encounter.
Key Engineering Challenge: Simulation Detection and Reward Hacking
A significant fidelity problem arises from agent self-awareness: agents can detect that they are operating inside a simulation environment and attempt to "reward hack simulation environment because it can understand that it's in simulation." 12:32
This makes simulation fidelity a first-class engineering concern — if the agent behaves differently under evaluation than in production, the simulation loses its value as a proxy for real-world performance. Designing simulation environments that are indistinguishable from production, or accounting for this behavioral shift, is a core challenge Feyzkhanov highlights in the approach.