AI agents are no longer science fiction — they’re becoming the backbone of modern startups. From automating workflows to scaling customer support, AI agents are transforming how teams build and operate products.
This post breaks down Google Cloud’s Startup Technical Guide: AI Agents, helping founders and developers understand what’s possible — and how to actually build, deploy, and scale AI agents efficiently.
📄 You can access the full PDF guide here: Startup Technical Guide: AI Agents (Google Cloud) ([go.cloudplatformonline.com][1])
🧠 What Are AI Agents?
AI agents combine large language models (LLMs) with tools, memory, and reasoning to perform complex, multi-step tasks.
Instead of just answering questions, agents can:
- Plan goals like “launch a product campaign” or “analyze customer feedback.”
- Take actions using APIs, data sources, or even other agents.
- Learn from context to improve decisions.
This marks a new era of “agentic” workflows — where AI doesn’t just respond, it acts.
⚙️ Core Components of Every AI Agent
Every powerful agent includes five key layers:
- Model (The Brain) – Choose the right model from the Gemini family:
- 💨 Gemini 2.5 Flash-Lite → fast, low-cost for simple tasks
- ⚖️ Gemini 2.5 Flash → balanced performance for production
- 🧩 Gemini 2.5 Pro → advanced reasoning for complex workflows
Tools (The Hands) – APIs, functions, or even other agents that execute tasks.
Example:process_refund(),get_customer_data(), orsend_slack_alert().Memory (The Mind) – Long-term and short-term storage for context and state.
- Use Vertex AI Search for retrieval (RAG)
- Firestore for session memory
- BigQuery for analytics
- Memorystore for caching
Orchestration (The Logic) – The reasoning framework, like ReAct (Reason + Act), that determines what to do next.
Runtime (The Body) – The infrastructure that runs agents reliably:
- Vertex AI Agent Engine – managed production runtime
- Cloud Run – serverless deployment
- GKE – scalable containerized infrastructure
🧩 Grounding: Making AI Reliable and Trustworthy
Grounding ensures your agent’s responses are factually accurate and verifiable.
✅ RAG (Retrieval-Augmented Generation)
Connect your LLM to real-time data with vector databases (like Vertex AI Search) so it doesn’t “hallucinate.”
🔍 GraphRAG
Adds relationships between data points, giving your agent a deeper understanding of context.
🤖 Agentic RAG
The next level — agents that reason about how to find the right data before answering.
“Agentic RAG turns your AI from a passive responder into an active researcher.”
🧰 The Complete Toolkit for Building Agents
Google Cloud’s Agent Development Kit (ADK) sits at the center of this ecosystem.
It’s open-source, code-first, and integrates directly with Google Cloud.
💡 With ADK, You Can:
- Build complex, multi-agent systems
- Integrate with existing tools (Slack, Notion, CRMs)
- Debug and evaluate agent reasoning
- Deploy fast using AgentOps + Vertex AI Agent Engine
ADK includes several agent types:
- LlmAgent – reasoning and decision-making
- SequentialAgent – executes sub-agents in order
- ParallelAgent – runs multiple agents simultaneously
- LoopAgent – repeats tasks until a condition is met
-
CustomAgent – your own logic via
BaseAgent
🌐 Interoperability: The Future Is Collaborative
Two emerging open standards make agent ecosystems interoperable:
- Model Context Protocol (MCP) – standardizes how agents access data/tools
- Agent2Agent (A2A) – enables multiple agents to communicate seamlessly
With these, startups can build agent networks that share context and delegate tasks automatically — think “multi-agent orchestration” for startups.
🛡️ Responsible & Reliable AI Agents
The guide also introduces AgentOps — a framework for production-ready AI.
AgentOps helps:
- Monitor performance
- Enforce safety and compliance
- Audit agent reasoning and outputs
- Evaluate results before scaling
☁️ Google Cloud’s Agent Ecosystem
Here’s a quick view of the AI stack startups can use:
- 🧠 Gemini Code Assist – for developer productivity
- ☁️ Gemini Cloud Assist – for infrastructure management
- 📊 Gemini in Colab Enterprise – for data science workflows
- 🧰 Google Agentspace – a no-code platform to build and manage agents
- 🧩 Agent Garden – deploy pre-built ADK agents
🔑 Key Takeaways
| Goal | Best Tool / Strategy |
|---|---|
| Build intelligent workflows | ADK + ReAct orchestration |
| Keep answers factual | RAG / GraphRAG grounding |
| Automate actions | Connect APIs & tools |
| Scale reliably | Vertex AI Agent Engine / Cloud Run |
| Manage data memory | Firestore + BigQuery + Memorystore |
| Collaborate across agents | MCP + A2A protocols |
| Ensure reliability | AgentOps monitoring |
🚀 Ready to Build?
Google Cloud offers everything you need — from open-source tools to $350K in startup credits through the Google for Startups Cloud Program.
If you’re a startup founder, engineer, or AI enthusiast — now’s the time to experiment, build, and scale with agents that think, act, and collaborate.
💬 “Mastering agentic AI is like building a team that never sleeps — one that learns, reasons, and works for you 24/7.”
🔗 Resources & References
- Agent Development Kit (ADK)
- Vertex AI Agent Engine
- Google Agentspace
- Model Context Protocol (MCP)
- Agent Starter Pack
- Full PDF Guide: Startup Technical Guide: AI Agents
Top comments (0)