Kamya Shah

TL;DR Distributed tracing gives end-to-end visibility across multi-agent and microservice workflows, making it practical to debug complex LLM applications, measure quality, and ship with confidence. By adopting observability-driven development with Maxim AI—spanning experimentation, simulation, evaluation, and real-time tracing teams can correlate prompts and tool calls, analyze agent trajectories,

TLDR AI observability is the practice of collecting, analyzing, and acting on comprehensive data signals from AI applications to understand system behavior, debug issues, and optimize performance. Unlike traditional software observability, AI observability addresses unique challenges including language model behavior, vector embeddings, multi-step agentic workflows, and non-deterministic outputs. By implementing

TLDR As AI agents become more complex and production deployments scale, observability has shifted from optional to essential. In 2025, enterprises require platforms that provide distributed tracing across agent systems, automated quality monitoring, debugging capabilities, and multi-modal support. Maxim AI stands out with its comprehensive full-stack approach combining experimentation, simulation,