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This week at Maxim, we’ve rolled out some powerful updates focused on dynamic scripting, enhanced LLM flexibility, and deeper error visibility across our SDKs and libraries. Script-Driven Variables in Evaluations Evaluations just got a whole lot smarter! All variables defined via custom scripts are now surfaced directly in workflow

Communication protocols play a crucial role in enabling seamless interactions between different systems. Google's recently published Agent to Agent Protocol (A2A) represents a significant advancement in this space, designed specifically to facilitate collaborative scenarios between autonomous agents. What is A2A? The protocol is an open specification that enables

Introduction In the realm of AI, especially with large language models, the prevailing belief is that complex reasoning tasks require detailed, step-by-step "thinking" processes. These processes often involve generating extensive intermediate steps before arriving at a solution, consuming significant computational resources. However, the paper titled "Reasoning Models

Introduction Healthcare assistants are changing the way patients and clinicians interact. For patients, these tools offer easy access to timely advice and guidance, improving overall care and satisfaction. For clinicians, they reduce administrative tasks, allowing more time for patient care while providing real-time knowledge and data to support informed decision-making.

Chain-of-Thought (CoT) based reasoning has exploded across the AI landscape. Modern large language models (LLMs) like Claude 3.7 Sonnet and DeepSeek R1 no longer just give answers but also generate natural language explanations that walk through their decision-making process. This transparency isn’t just about UX but it has

In this recent paper, The Era of Experience, David Silver and Richard Sutton articulate a vision for artificial intelligence where there is a shift from reliance on static, human-generated data to dynamic, self-generated experiential learning. This paradigm aims to propel current models beyond their limitations, developing systems capable of continuous

In this recent paper, The Era of Experience, David Silver and Richard Sutton articulate a vision for artificial intelligence where there is a shift from reliance on static, human-generated data to dynamic, self-generated experiential learning. This paradigm aims to propel current models beyond their limitations, developing systems capable of continuous

Medical note-taking and scribing assistants have eased the administrative burden on clinicians by ambiently generating clinical notes, allowing them to spend less time documenting and more time with patients. In high-stakes clinical environments, it is essential to generate clear, accurate, and safe notes based on multi-turn interactions between clinicians and

Through the first two blogs (Part 1 and Part 2) of the AI agent evaluation series, we explored AI agents and the key performance metrics for evaluating them. Now, we focus on building end-to-end evaluation workflows. A structured AI evaluation process encompassing both pre-release and post-release phases is crucial for

Prompt engineering might just be one of the most accessible yet impactful skills in artificial intelligence today. At its core, prompt engineering involves crafting specific inputs that guide Large Language Models (LLMs) to produce precise and accurate outputs, essential for ensuring AI quality. You don't have to be

In recent months, we've witnessed an explosion in the development of AI agents. Autonomous systems powered by large language models (LLMs) can perform complex tasks through reasoning, planning, and tool usage. However, as the field rapidly advances, a critical question emerges: how do we effectively measure and compare

As language models become increasingly agentic, including browsing the internet, reasoning across sources, and acting on user instructions, it is imperative that our methods of evaluating their capabilities must evolve too. OpenAI’s BrowseComp introduces a fresh benchmark for this paradigm, offering a challenging, realistic, and carefully curated evaluation framework

Intro: Why this matters Picture this: You’re building an AI agent to help users book travel, troubleshoot software, or manage finances. One task leads to another. The user changes their mind halfway. New context unfolds mid-conversation. How do you train your AI to navigate all of that without losing