From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI
Source
AI Engineer
Author
AI Engineer
Date
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
Why it matters
Public AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → benchmarks measure someone else's tasks; this is the concrete method for building the evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → that matches your own production environment, plus the failure modes to expect.
Key quotes
“Basically, public benchmark is useful to orient and build your prior, but your private benchmark is useful to ship.”
Rustem Feyzkhanov
“because in your production, you don't care about the model, you care about the full system.”
Rustem Feyzkhanov
“Agent can try to reward hack simulation environment because it can understand that it's in simulation and it can hack it.”
Rustem Feyzkhanov
“effectively benchmark is software. It's code. It's files. You need to treat it as such. You need to have a separate CI pipeline for it.”
Rustem Feyzkhanov
“with simulation, you control the full stack. You can evaluate the full stack. And you can make sure that fix lives in the correct place.”
