How Google Runs GenAI in Production (and What You Can Steal)

Everyone’s hyped about GenAI. But what does it really take to build a scalable, observable, and maintainable GenAI system?

Google’s new whitepaper on operationalizing GenAI on Vertex AI gives us the blueprint—buried under 90 pages of enterprise speak.

In this post, I’m pulling out the real lessons. The things that you need to know to avoid the most common pitfalls and build like Google (without their infra budget).

⚙️ 1. Prompts Are Code, Data, and Configuration—All at Once

We’re entering the era of prompt operationalization.

In GenAI systems, prompts are no longer throwaway strings. They're versioned, structured, and deeply tied to performance.

🔍 Why this matters:

• Prompt templates evolve alongside your app logic

• One prompt version might work with Llama 2, but break with Gemini 1.5

• Few-shot examples are data—and should be validated like input datasets

💡 How to handle it:

• Store prompt templates in version control (use Git + YAML or JSON)

• Annotate prompts with metadata: use case, model version, expected behavior

• Build tooling to test prompt changes just like unit tests (TruLens or LangSmith)

🔗 2. Chaining Models = Software Engineering, Not Magic

The whitepaper emphasizes: GenAI chains are not pipelines—they're reactive programs.

Each component (prompted model, retrieval step, tool call) depends on the behavior of the previous step.

📉 When things break, it's rarely one big failure—it’s silent performance degradation due to:

• Prompt drift

• Retrieval mismatches

• Untracked tool call failures

🧰 Tooling stack to survive this:

• LangChain Tracing or OpenTelemetry for step-by-step monitoring

• Chain versioning: Treat your chain like a deployable artifact with clear inputs/outputs

• Log intermediate outputs, not just final responses

🎯 3. Evaluation Isn’t Optional—It’s Your Debugger

Most teams still eyeball GenAI results and call it “good enough.” That’s fine in dev. But not in production.

📈 The whitepaper breaks evaluation down into:

• Prompt-level evaluation (AutoSxS, LLM-as-Judge, human raters)

• Chain-level evaluation (end-to-end metrics + trajectory analysis)

• Agent-level eval (tool success rate, reasoning correctness, recovery from errors)

🛠️ Set up a 3-tier evaluation loop:

1. Synthetic prompts → auto-judged with LLMs

2. Real user data → curated and scored

3. Edge cases → manually maintained, tracked over time

🔁 Bonus: Collect production outputs into your eval dataset over time (continuous evaluation).

🧠 4. Tool Registries Are the New APIs

Agents need tools. But throwing 50 APIs into your agent and calling it “autonomous” is asking for chaos.

📚 Google’s solution: Tool Registries

• Think of it like a Postman collection + OpenAPI spec for GenAI agents

• Each tool: metadata, version, input schema, output expectations, permissions

🔍 Why it matters:

• Agents hallucinate less when tools are well-described

• You can trace which tool failed and why

• Lets you enforce access, observability, and debugging per tool

🧠 Pro tip:
Use tool registries + curated tool lists to define:

• Generalist agents → broad tools, less predictable

• Specialist agents → narrow toolset, safer, easier to monitor

🤖 5. Agents Need CI/CD Too

You wouldn’t deploy an API without CI/CD. Why do it with agents?

Google’s pipeline:

• Repo with agent prompt, tool config, function schemas

• Unit tests for each tool

• Canary tests in staging with sample tasks

• Manual QA + auto eval loop

• 🚀 Deploy with confidence

👀 Best practice for founders and builders:

• Use GitHub Actions or Temporal for orchestrating builds and tests

• Run scripted tests like:
  Test: For input X, does agent use tool A and produce Y?

📉 Bonus: Monitoring = Logging + Drift Detection + Human Feedback