Building Agentic AI on Google Cloud

The next wave of enterprise AI is agentic—systems capable not only of generating outputs, but reasoning, planning, and acting autonomously across complex workflows. While Large Language Models (LLMs) laid the foundation, Agentic AI builds the intelligence layer that turns models into doers. I think one of the easiest ways to create a production ready Agentic AI app is with AWS.

1. What Is Agentic AI?

Agentic AI refers to architectures where multiple specialized AI “agents”—each with unique goals, memory, and tools—collaborate to execute tasks with minimal human supervision. These systems can:

• Plan multi-step workflows dynamically

• Retrieve and synthesize domain-specific data

• Invoke APIs, databases, or other tools

• Critique their own outputs and improve over time

Frameworks like LangGraph, CrewAI, and AutoGPT are evolving rapidly, but Google provides the enterprise backbone for scaling them securely.

2. Google Cloud as the Agentic Backbone

• Google Cloud provides an ideal foundation for building modern agentic systems, combining scalable model hosting, robust data services, and event-driven orchestration.

Layer	GCP Service	Role
Model Hosting	Vertex AI (Model Garden, Custom Models), Gemini APIs	Serve, fine-tune, and orchestrate models such as Gemini, Llama, Claude (via partners), or your own custom models
Memory & Context	Firestore / Cloud Bigtable, AlloyDB / Cloud SQL, Vertex AI Vector Search, Cloud Storage	Persistent state, hierarchical memory, and vector embeddings for agent recall
Reasoning Orchestration	Cloud Functions, Cloud Run, Workflows, Eventarc	Drive multi-agent reasoning loops and trigger toolchains
Tool Access Layer	API Gateway, Cloud Endpoints, Secret Manager, Cloud Run / GKE	Expose external APIs, tools, and actions with secure routing
Observability	Cloud Logging, Cloud Monitoring, Cloud Trace, Vertex AI Explainability / Guardrails	Logging, evaluation, drift detection, and compliance monitoring

3. Architecture Blueprint

A production-grade Agentic AI system on Google Cloud could look like this:

• Planner Agent (Gemini in Vertex AI)Breaks down the user request into structured subtasks and required tools.

• Retriever Agent (Cloud Run + Vertex AI Vector Search)Retrieves enterprise context—documents, embeddings, transactional data—across structured and unstructured stores.

• Executor Agent (Vertex AI custom model endpoint or Cloud Run microservices)Runs domain-specific logic such as claims adjudication, underwriting, F&A workflows, or document extraction.

• Reviewer Agent (Gemini + Firestore Memory Layer)Evaluates outputs, checks for compliance, improves reasoning steps, and applies reinforcement from user signals.

• Orchestrator (Workflows + Eventarc)Manages inter-agent communication, retries, state management, and system reliability.

This modular, service-oriented design supports explainability, cost governance, and near-infinite horizontal scaling using Google’s global infrastructure.

4. Why This Matters

Agentic AI elevates AI from a conversational tool into a scalable digital workforce. Deployed on Google Cloud, agentic systems can:

• Automate 60–80% of manual cognitive operations across insurance, healthcare, financial services, and back-office workflows

• Self-improve via continuous learning loops and memory-augmented reasoning

• Run in secure, regulated cloud environments with enterprise-grade controls

• Deliver domain-specific intelligence at global scale using Google’s AI-native platform

  
  

The future of AI isn’t just about bigger models—it’s about smarter agents running on trusted infrastructure.