Kuldeep Paul

Modern AI applications (chatbots, copilots, RAG systems, and voice agents) live and die by the quality of their evaluations. If you cannot trust your evals, you cannot trust your releases. The most reliable way to achieve trustworthy AI evaluation is to curate a high-quality “golden dataset” that mirrors production reality,

Multi-agent systems have become the standard architecture for complex AI applications. However, as these systems grow more sophisticated, understanding their behavior in production becomes increasingly challenging. Without proper observability and evaluation, teams face issues ranging from unexpected agent handoffs to degraded response quality, problems that only surface after deployment. This

Google’s Agent Development Kit (ADK) makes it straightforward to design multi‑agent systems, while Maxim provides the end‑to‑end stack for simulation, evaluation, and observability required to ship these systems reliably. This guide shows how to combine ADK and Maxim for robust agent tracing, debugging, and continuous quality

Designing reliable, production‑grade multi‑agent systems requires more than getting a demo to run. It demands deep agent observability, systematic agent evals, disciplined prompt management, and a scalable AI gateway strategy, implemented step by step, with traceability and measurable quality. This practical guide shows you how to instrument a

AI agents are increasingly expected to behave consistently, remember context, and improve over time. Yet most large language models (LLMs) operate within short context windows and stateless APIs, making durable memory and continuity non-trivial. This blog systematically unpacks what “long-term memory” means for AI agents, why it is hard, which