In today’s newsletter, I am breaking down the guide from Google to build and scale AI Agents.

In today’s edition:

• AI Case Study— Google’s Guide to Build and Scale AI Agents

• Build Together— Here’s How I Can Help You

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[AI Report Analysis]

Google’s Guide to Build and Scale AI Agents

AI is moving from chatbots to full agent systems that don’t just answer questions, but can plan, take actions, and complete multi-step tasks.

Google recently released a detailed guide for startups on how to build, scale, and govern AI agents.

I’ve gone through it and pulled out the most useful insights for you.

Think of this edition as your shortcut instead of reading a 60-page technical guide, you’ll get the key takeaways here.

Core Components of an AI Agent

Google breaks down an agent into a few building blocks:

• Model – usually an LLM like Gemini or an open-source model

• Tools – functions the agent can call (APIs, databases, CRMs)

• Memory – short-term (conversation state) and long-term (databases like Cloud SQL or Spanner)

• Orchestration – the executive function that decides what to do next. A common pattern here is ReAct (Reason + Act), where the model alternates between thinking and calling tools.

• Grounding – using RAG or knowledge graphs to reduce hallucinations.

• Runtime – the infrastructure to actually deploy and run agents reliably.

Start with these 2 if you're learning AI Agents:

• AI Agents 101 - Everything You Need To Know About Agents

• AI Agents vs LLMs

Key Tools Google Highlights

ADK (Agent Development Kit)

• a code-first toolkit to build custom agents

• good for technical teams that want fine-grained control

Google Agentspace

• a no-code platform for non-engineers to build agents and manage them at scale

• helpful for org-wide adoption

Gemini Code Assist

• developer-focused AI that helps write, debug, and refactor code across IDEs, GitHub, and the command line

Vertex AI RAG Engine

• google’s managed service for retrieval-augmented generation

• helps ground answers in your company’s data

Agent Starter Pack

• templates, CI/CD, observability, and evaluation tooling so you don’t just build agents, but also monitor and improve them in production.

Building vs Using vs Partnering

Google sees three main paths for startups and enterprises:

1. build your own (using ADK) more control, but more effort.

2. use Google’s pre-built agents like Gemini Code Assist or Gemini Cloud Assist

3. bring partner agents integrate open-source or third-party agents into your stack

Don’t reinvent the wheel.

Use the right mix of these depending on your resources and stage.

AgentOps

A big part of the guide is about trust and evaluation.

• don’t rely on vibe testing (just trying prompts)

• instead, use structured evaluation across four layers:

  • component-level (test each tool s

    eparately)

  • trajectory-level (test the agent’s steps (Reason/Act/Observe)

  • outcome-level (check final answers for accuracy, grounding, & helpfulness

  • system-level (monitor live performance, failures, latency, and drift)

This is where Google’s Agent Starter Pack helps with CI/CD, automated tests, monitoring, and dashboards.

If you only remember 20 things about AI Agents, let it be these

1) ai agents change how software is built and used
2) agents can plan and act, not just answer questions
3) build custom agents for control, use managed agents for speed
4) pick models by balancing power, cost, and speed
5) use different models for different sub-tasks
6) orchestration makes agents effective problem solvers
7) grounding protects your agents from hallucinations
8) rag is the simplest step toward reliable outputs
9) agentic rag adds planning to information retrieval
10) use agents as tools to delegate tasks
11) open protocols future-proof your agents
12) evaluation layers catch problems early
13) trajectory analysis reveals hidden errors
14) launch with confidence using evaluation and monitoring tools
15) automate workflows instead of single tasks
16) combine short-term and long-term memory for personalization
17) break down data silos with no-code agentspace
18) free engineers from cloud tasks with ai assistants
19) production-ready agentops builds long-term success
20) the future uses multiple llms connected by context

Download the guide from Google here.

Before you go: Here’s How I Can Help You

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