[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-agent-simulation::en":3,"gloss-cluster-agent-simulation::en":23,"gloss-next-agent-simulation::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"agent-simulation","agents","Agent Simulation","Agent simulation is running an agent against a stand-in for the real world — recorded or synthetic tool responses, a copy of the data, outbound calls that go nowhere — in order to observe its behaviour before that behaviour has consequences. It answers a different question from a sandbox, which contains what the agent does, and from a dry run, which shows what a single operation would do. Simulation asks what the agent will decide across a whole task.\n\nIt exists because agents cannot be tested the way ordinary code is. There is no fixed input-output pair to assert on when the system chooses its own sequence of steps, so the unit worth examining is the path taken rather than the final answer. Simulation is how you get paths cheaply and repeatedly, including paths through situations that are rare, expensive or dangerous to produce on purpose in production.\n\nWhat the simulation reproduces determines what it can tell you. Tools that always succeed and return tidy data will produce an agent that looks reliable and has never encountered a timeout, a rate limit, an empty result, a permission denial, or a field containing something nobody expected. The failures are the point, and they need to be in the fixtures deliberately, because the real environment supplies them on its own schedule and usually at the worst moment.\n\nRecorded runs make the technique compound. Trajectories captured from real usage, replayed against fixed responses, become a regression suite: change a prompt, a model or a tool description, and you can see which previously working paths now diverge. Without that, every change to an agent is evaluated by trying it a few times and forming an impression, which does not survive contact with a system that behaves differently on identical input.\n\nThe limit is worth stating so the technique is not oversold. A simulation contains only the situations somebody thought of, and the situations nobody thought of are the ones that cause incidents. It reduces the number of surprises that reach production; it does not turn an unpredictable system into a predictable one, and it is not a substitute for limits, logging and a way to stop a run that is going wrong.","Agent simulation runs an agent against recorded or synthetic tools to see the path it takes. The failures have to be in the fixtures, or it proves nothing.",null,[11,14,17,20],{"slug":12,"name":13},"agent-sandbox","Agent Sandbox",{"slug":15,"name":16},"agent-trajectory","Agent Trajectory",{"slug":18,"name":19},"regression-testing","Regression Testing",{"slug":21,"name":22},"sandbox-environment","Sandbox Environment",[24,28,32,35,38,41,44,47,48,49,52,55],{"slug":25,"category":5,"name":26,"updated_at":27},"acceptance-criteria","Acceptance Criteria","2026-09-08T21:32:37+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"agent-budget","Agent Budget","2026-09-08T21:32:36+00:00",{"slug":33,"category":5,"name":34,"updated_at":31},"agent-checkpointing","Agent Checkpointing",{"slug":36,"category":5,"name":37,"updated_at":31},"agent-handoff","Agent Handoff",{"slug":39,"category":5,"name":40,"updated_at":27},"agent-identity","Agent Identity",{"slug":42,"category":5,"name":43,"updated_at":31},"agent-loop","Agent Loop",{"slug":45,"category":5,"name":46,"updated_at":31},"agent-memory","Agent Memory",{"slug":12,"category":5,"name":13,"updated_at":31},{"slug":15,"category":5,"name":16,"updated_at":31},{"slug":50,"category":5,"name":51,"updated_at":31},"agentic-rag","Agentic RAG",{"slug":53,"category":5,"name":54,"updated_at":27},"approval-fatigue","Approval Fatigue",{"slug":56,"category":5,"name":57,"updated_at":31},"computer-use","Computer Use"]