Simulation

AI agents are transforming enterprise operations through autonomous decision-making, multi-turn conversations, and dynamic tool usage. However, their non-deterministic nature creates significant challenges for quality assurance and reliability. Unlike traditional software systems where identical inputs produce identical outputs, AI agents generate varied responses even under identical conditions, making conventional testing approaches

TL;DR Testing AI agents requires fundamentally different approaches than traditional software testing. Current evaluation of AI agents is predominantly focused on accuracy metrics that measure task completion success, but this offers an incomplete picture of overall agent performance and utility. Effective testing frameworks must evaluate not just outputs, but

As AI agents support more customer interactions, operational workflows, and multi step tasks, the need for predictable and reliable behavior increases sharply. A single incorrect reasoning step, an invalid tool call, or a loop that never terminates can disrupt user experience or create compliance-related exposure. This has made AI agent

TL;DR: Scenario-based simulation is the reliability layer AI teams need in 2026. By running multi-turn, persona-driven conversations against realistic scenarios with defined steps, tools, and context, and scoring them with evaluators, teams expose failure modes early, harden safety and policy adherence, and make data-backed release decisions. Maxim unifies this

AI agents deployed in production face unpredictable user interactions, edge cases, and failure modes that are difficult to anticipate during development. Traditional testing approaches (unit tests, integration tests, and manual QA) prove insufficient for systems that generate non-deterministic outputs, handle unstructured inputs, and make autonomous decisions. When quality issues emerge

TLDR; Multi-turn simulation exposes failure modes you’ll miss with single-turn tests. Using structured scenarios, personas, and evaluator-driven analysis across datasets, teams can track metrics such as step completion, overall task success, adherence to instructions, and conversational drift in longer interactions. Maxim AI provides end-to-end capabilities: simulation, evaluation, and observability,