3 posts tagged with "Observability"

The progression of Generative AI from novelty to enterprise cornerstone has necessitated a fundamental shift in system construction methodology. Early LLM adoption emphasized "prompt engineering"—ad-hoc string concatenation, trial-and-error phrasing, and minimal state management. While sufficient for simple chatbots, this approach collapses under production demands: reliability, observability, latency, and cost-efficiency.

The Google Gen AI Agent Development Kit (ADK) signals the arrival of Context Engineering—a discipline that treats context not as a mutable string buffer but as a compiled view over rich stateful systems.

Building a GenAI agent prototype on your laptop is magic. You write a few lines of Python, hook up an LLM, and suddenly you’re chatting with your data. But taking that magic from a Jupyter notebook to a production environment—secure, scalable, and observable—is where the real headache begins.

Enter the Google Cloud Agent Starter Pack.

This open-source repository is Google’s answer to the "prototype purgatory" problem. It’s a comprehensive toolkit designed to bootstrap production-ready generative AI agents on Google Cloud Platform (GCP) in minutes, not months.

You build an AI agent with Google ADK. It works. But when you ask "Why did the agent choose that tool?" or "Which LLM call took 5 seconds?" – you're flying blind.

Enter distributed tracing: Jaeger visualizes every step your agent takes, from reasoning to tool execution to LLM calls. ADK has built-in OpenTelemetry support, making this a breeze... once you understand one crucial gotcha.

This post shows you the complete picture: what to do, why it matters, and the one thing that trips up most developers.