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Introduction Building AI agents has become more accessible than ever, empowering developers to create sophisticated, autonomous systems. But moving from a working prototype to a production-ready agentic application brings a new set of challenges, from ensuring reliability and safety, to evaluating performance at scale. Agentic systems, by nature, are complex.

What if you could test your AI system with thousands of diverse users without recruiting a single person? User Simulation makes this possible. Simulating human users - a fundamental application of AI has driven progress in both research and industry. By allowing machines to imitate real user interactions, user simulation

Ever wanted to create an intelligent game that can generate questions, check answers, and adapt to different difficulty levels? In this tutorial, we'll build a Math Trivia Game using Mistral AI's language model and Maxim for observability. Our agent will be able to generate arithmetic and

Last week at Maxim, we rolled out several powerful upgrades to give teams more control, clarity, and customization across the platform. Here's what’s new: Custom Dashboards Just Got an Upgrade Dashboards are now more flexible and insightful: * Custom metric cards – Build exactly what you need to monitor

Picture this scenario: You’re very new to AI, exploring chatgpt by testing its capabilities on various topics, expecting honest answers unaware that behind the scenes, it already figured out that it’s being tested and is subtly changing its behaviour to ace your tests. This feels like a subtle

“How’s the weather today in Delhi?” Simple question - but what if we wanted a conversational AI that could answer it, explain the temperature trend, and log every detail of its interaction for analysis? Agentic systems are booming. But building a reliable production-ready AI agent involves more than just

“How’s the weather today in Delhi?” Simple question - but what if we wanted a conversational AI that could answer it, explain the temperature trend, and log every detail of its interaction for analysis? Agentic systems are booming. But building a reliable production-ready AI agent involves more than just

Introduction Consider a scenario: You're facing a complex optimization challenge with no known solution - the kind that requires inventing entirely new algorithms, not just tweaking existing ones. There's no textbook answer, no established approach. Existing coding models like Claude, Gemini 2.5 can implement known

When you’re building with LLMs, day-to-day tasks like writing, brainstorming, and quick automation feel almost effortless. But as soon as you try to construct a robust, production-grade pipeline, the real challenges emerge. One of the first hurdles is interface fragmentation: every provider exposes a different API, with its own

Introduction Vision-Language Models (VLMs) have achieved remarkable success in tasks such as coding and mathematical reasoning, often surpassing human performance. However, their ability to perform tasks that require human-like perception, spatial navigation, and memory management remains underexplored. To address this gap, the paper titled "VideoGameBench: Can Vision-Language Models complete

This comprehensive guide will walk you through creating an intelligent events discovery agent (an agent that discovers public events happening in the US) using n8n (an open-source workflow automation platform) and rigorously testing it with Maxim (an agent testing platform). What We'll Build We’re going to create

Introduction As large language models (LLMs) continue to improve in tasks ranging from education to enterprise automation, alignment techniques like Reinforcement Learning from Human Feedback (RLHF) have become the standard. These methods make models safer, more helpful, and generally better at following instructions. However, recent findings challenge the assumption that