Production-Ready AI Agents: 5 Lessons from Refactoring a Monolith- Google Developers Blog

What this post added

This post details the refactoring of a monolithic sales research AI agent ('Titanium') into a production-ready system using Google's Agent Development Kit (ADK). Key technical contributions include: 1. Replacing the monolithic script with an orchestrated pipeline of specialized sub-agents (Company Researcher, Search Planner, Case Study Researcher, Selector, Email Drafter) using ADK's `SequentialAgent`. 2. Enforcing structured outputs by replacing prompt-based JSON formatting with Pydantic objects, leveraging ADK's dynamic Structured Outputs feature. 3. Implementing a dynamic Retrieval Augmented Generation (RAG) pipeline by using Playwright for web scraping and Google Cloud Vector Search for indexing and hybrid search of customer success stories, replacing hardcoded case studies. 4. Integrating OpenTelemetry via ADK for observability, capturing distributed traces of model requests, tokens, and tool executions, and pairing it with Server-Sent Events (SSE) for live telemetry. 5. Leveraging ADK's built-in cost optimization features like exponential backoffs, timeout boundaries, and configurable retry loops to manage token burn.