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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

Imagine asking an AI, “What’s the best route to work tomorrow morning?” and receiving a response that not only understands your intent but actually checks live traffic data via an external API and gives you the optimal route. This isn’t a future concept. It’s happening right now—

You: Hey AI, what did Prime last tweet about? AI: I don’t have access to Twitter / X. You: Okay, whatever—just tell me if Vercel has any incidents right now. AI: I can’t browse the internet. Bruh. At this point, you’re wondering why this “intelligent” model can

This is Part 2 of our Agent Evaluations series. Here are Part 1 and Part 3 in this series. As AI agents start to gain traction across industries, driving innovation in tasks ranging from customer support to automation of tasks like booking requires their real-world performance evaluation to go beyond

This is Part 1 of our Agent Evaluations series. Here are Part 2 and Part 3 in this series In today’s rapidly advancing world of artificial intelligence (AI), agentic systems are becoming an integral part of numerous industries, powering everything from customer support to robotics. But what exactly are

Introduction Unlike deterministic systems, GenAI introduces fundamental unpredictability to production environments. Traditional observability tools fail to capture the semantic drift, hallucination patterns, and prompt-dependent behaviors inherent to large language models. While conventional systems map cleanly from input to output, GenAI requires monitoring the unexplored territory between what we asked for

In today’s data-driven world, businesses increasingly rely on sophisticated querying systems to extract valuable insights. The complexity of writing SQL Queries can be a barrier, particularly for non-technical teams. Uber has revolutionized this process by leveraging GenAI to empower its teams to generate accurate SQL queries simply by asking

What is RAG? Retrieval-augmented generation (RAG) is a process designed to enhance the output of a large language model (LLM) by incorporating information from an external, authoritative knowledge base. This approach ensures that the responses generated by the LLM are not solely dependent on the model's training data.