Most AI agents never make it to production.

Google published a playbook for crossing that gap.

I personally use Gemini, Vertex, and GCP for AI systems right now.

Gemini 3 Flash is a world-leading model that's both fast and cheap. The Agent Development Kit makes building agents easy - with a built-in web UI that makes development a joy.

And it's available for Java, Python, TypeScript, and Go. Not just Python.

This guide shows how Google thinks about production agent architecture.

Here are the key frameworks:

1. Three types of memory

Production agents need all three:

→ Long-term: Knowledge retrieval (RAG, vector search)
→ Working: Conversational context (session state)
→ Transactional: Durable action logs (database records)

Most demos only have working memory. That's why they break.

2. Four-layer evaluation

You can't test agents like normal software:

→ Layer 1: Unit tests for tools and APIs
→ Layer 2: Trajectory correctness (did it reason right?)
→ Layer 3: Outcome correctness (is the answer accurate?)
→ Layer 4: System metrics (latency, failure rates)

Most teams stop at Layer 1. Production requires all four.

3. AgentOps

DevOps and MLOps adapted for agents.

Infrastructure as code. CI/CD pipelines. Observability from day one.

At the end of the day, AI engineering requires learning great software engineering and architecture.

Google is becoming a serious contender in AI. This guide shows why.