Simulation

AI agents are moving from demos to production. When that happens, quality has to be intentional. Real users bring edge cases, messy context, ambiguous goals, and time pressure. The fastest way to harden an agent without burning weeks of manual QA is simulation: repeatedly stress-test the agent across realistic scenarios,

TL;DR AI agent simulation is the fastest, safest way to pressure-test your agents before they touch production. By simulating multi-turn conversations across realistic scenarios and user personas, you can find failure modes early, measure quality with consistent evaluators, iterate confidently, and wire results into CI/CD for guardrailed releases.

Agent simulation is the practice of testing AI agents in controlled environments that approximate multi-turn user interactions, tool usage, and varied personas. The purpose is to reveal failure modes and measure end-to-end quality before and after release. This guide outlines core concepts, scenario design, metrics, and workflow integration, with references

Generative AI agents aren’t just answering questions. They keep track of context, call APIs, enforce policies, and handle sensitive data. If you ship without proper testing, expect hallucinations, privacy leaks, and broken flows. Agent Simulation in Maxim makes quality control systematic, run data-driven tests, catch failures before users do,