3 posts tagged with "Architecture"

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.

You just built an amazing AI agent. It works perfectly locally. You've tested it with your team. Now comes the question that keeps you up at night:

"How do I actually deploy this thing to production?"

You Google it. You find 47 different opinions. Some say "use Kubernetes." Others say "just use serverless." One person mentions "you definitely need a custom FastAPI server." Another says you absolutely don't.

What you need is clarity. Not complexity. That's what this guide gives you.

The multi-agent pattern using specialized agents as tools isn't primarily about raw performance gains—it's fundamentally about managing complexity and cognitive workload. Here's why this matters: