11 posts tagged with "Google ADK"

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 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.

Your agent's instructions are its Standard Operating Procedure (SOP). In Google ADK, this SOP lives in the agent's prompt—the detailed instructions that guide every decision, every tool call, every response.

The problem? Writing the perfect SOP manually is nearly impossible. You add rules to fix failures. Each new rule breaks something else. Your agent becomes unpredictable. Your SOP becomes a mess of band-aids.

The solution? GEPA (Genetic Evolutionary Prompt Augmentation)—automatic SOP optimization that learns from failures and evolves better instructions through real testing.

Extend ADK with custom session storage backends like Redis, MongoDB, PostgreSQL, or DynamoDB for persistent sessions that survive server restarts.

Your agent gets the final answer right, but did it get there the right way? Learn how to evaluate tool selection and usage quality with ADK 1.16's rubric-based evaluation framework.

Long-running agent tasks need to be interrupted gracefully or paused for human feedback without losing progress. System failures can interrupt execution mid-task, causing work to be lost.

Long agent conversations accumulate thousands of tokens. After 100+ exchanges, sending the entire history to the model becomes expensive and slow. Context Compaction fixes this by intelligently summarizing old interactions.

In one sentence: Context Compaction automatically summarizes older conversation events using an LLM, reducing token usage while preserving conversational context.

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:

Update: December 2025

We've reached our final milestone: All 35 tutorials are now complete with working implementations. That's 100% coverage of the entire curriculum, taking you from your first agent to production deployment with real-world integrations.