Simulation

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

Building trustworthy AI no longer stops at model scorecards. In 2026, the standard for AI quality shifts decisively from static model benchmarks to agent-level evaluation, simulation, and observability across real user journeys. Teams need to understand multi-turn decisions, tool calls, retrieval context, and failure recovery, not just whether a model

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,

TL;DR Scenario-based testing makes AI agents reliable by validating behavior across realistic, multi-turn conversations, diverse personas, tools, and context sources. Traditional testing falls short because agents are non-deterministic, and multi-turn conversational. Maxim AI provides an end-to-end platform: Prompt IDE, agent simulations, unified evaluators, and enterprise observability to scale evaluations,