Simulation

Agent Simulation: A Technical Guide To Evaluating AI Agents In Realistic Conditions

Agent Simulation: A Technical Guide To Evaluating AI Agents In Realistic Conditions

Agent simulation is the practice of testing AI agents in controlled but realistic environments that mirror 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,

Agent Simulation & Testing Made Simple with Maxim AI

Agent Simulation & Testing Made Simple with Maxim AI

Generative-AI agents do more than answer one question, they maintain context, call external APIs, enforce refund policies, and handle sensitive data. Releasing such systems without systematic testing risks hallucinations, privacy breaches, and broken user journeys. Maxim’s Agent Simulation module turns quality assurance into a repeatable, dataset-driven discipline. This article

Simulate Before You Ship: 5 Agent-Simulation Scenarios That Save Money in Production

Simulate Before You Ship: 5 Agent-Simulation Scenarios That Save Money in Production

In the rapidly evolving world of AI-powered applications, agent-based systems are transforming how enterprises automate workflows, deliver customer experiences, and optimize operations. However, deploying AI agents directly into production environments without thorough testing can lead to costly failures, unexpected downtime, and diminished user trust. Simulation-driven development offers a solution: by