google cloud

GCP Architecture / Application Development 

Anatomy of Agentic System on Google Cloud

The anatomy of an agentic system on Google Cloud Platform (GCP) is structured around core functional layers—combining reasoning models, frameworks, tools, state, and security—deployed onto scalable cloud runtimes.

Core Architectural Layers on GCP

1. AI Models & Reasoning Brain

• Foundation Models: Uses Gemini Pro or Gemini Flash via managed GCP APIs (Vertex AI / Gemini Enterprise) for natural language comprehension, tool-selection logic, and planning loops (such as ReAct: Reason + Action). It manages the fundamental agent loop: Sense → Reason → Plan → Act → Reflect.

• Dynamic Model Routing: Selects lightweight models for simple subtasks and heavy reasoning models for complex planning or multi-agent delegation.

• Model Garden: Access to over 200 frontier models (including Google’s Gemini family, open-source models, and third-party options like Claude). 

2. Agent Development & Orchestration Frameworks (The Scaffolding)

• Agent Development Kit (ADK): Google's open-source framework used to build modular single-agent or multi-agent workflows, define agent units, handle execution runners, and manage lifecycle callbacks.

• Genkit / Custom Control: An open-source framework favored for building custom control flows and JavaScript/TypeScript or Go/Python workflows. Used for customized control flows and programmatic prompt-to-tool orchestration.

• Design Patterns: Implements single-agent, sequential, parallel, reviewer-critic, or coordinator/multi-agent topologies.

• Agent Studio: A low-code visual canvas for prototyping, designing reasoning loops, and refining prompts side-by-side.

• Agent Garden: A curated library of pre-built agent templates and multi-agent samples.

3. Tools & Capabilities Integration

Determines how the agent processes tasks. This can follow a Single-Agent System using a dynamic ReAct (Reason + Action) loop, or complex Multi-Agent Systems orchestrated through a central routing hub.

• Model Context Protocol (MCP): Standardized protocol used to securely connect agents to external data sources, internal microservices, and specialized tool servers. 

• Grounding Layer (RAG): Eliminates hallucinations by anchoring agents to enterprise data via Vertex AI Vector Search, BigQuery

• Agent-to-Agent (A2A) Protocol: Standardized communication allowing independent agents across application boundaries to coordinate, negotiate, and share data. [1]

• Native GCP Tools: Built-in connectors for BigQuery (generating GoogleSQL), AlloyDB for PostgreSQL (generating PostgreSQL dialects via MCP Toolbox), and Cloud Storage.

• Progressive Disclosure: Exposes tool definitions dynamically to prevent "tool bloat" and token overflow in the model context window.

4. Memory & State Management

• Short-Term Memory / Scratchpad: Manages active dialogue turn history, current plan steps, and immediate execution context.

• Long-Term Memory: Utilizes persistent data stores such as Firestore, Redis, Cloud SQL, or vector search indexes for semantic retrieval of past interactions and business semantics.

5. Runtimes & Infrastructure

The compute platforms where the agent lives and executes actions.

• Agent Gateway / Runtime: Centralized cloud-native portals enforcing edge policies, handling agent identities, and masking sensitive data via Model Armor.

• Fluid Compute: Flexible runtime orchestration across Cloud Run (for serverless agent microservices) and Google Kubernetes Engine (GKE)

6. Security, Governance & Guardrails

• Model Armor: Inspects prompts and model outputs for prompt injection, sensitive data leakage (PII masking), and harmful content.

• Agent Gateway / IAM: Enforces agent identity, least-privilege service accounts, and principal access boundaries (PAB).

• Network Security: Integrates VPC Service Controls, Cloud Armor, and Identity-Aware Proxy (IAP) for edge inspection and tenant isolation.

Three-Tier Testing Strategy

1. Tier 1: Component / Unit Tests

• Tests isolated components, single prompts, and individual tool definitions.

• Checks whether the agent selects the right tool and passes valid parameters.

2. Tier 2: Integration / Trajectory Tests

• Evaluates multi-step agent journeys and decision-making sequences.

• Measures trajectory matching (whether the agent executed the expected order of tool calls) and response match (semantic/lexical similarity to golden answers).

3. Tier 3: End-to-End & Human-in-the-Loop Reviews

• Evaluates multi-agent handoffs, complex reasoning, safety boundaries, and user goal satisfaction.

• Uses production traces or simulated user personas to surface edge cases

Certifications

Google Research / Experiments

Careers @ Google Cloud

Interviews @ Google Cloud

How to prepare for Software Engineer Interview ?

Become GCP Partner

First, please apply to be a GCP partner.

Build Engagement Model

Next you'll want to register your GCP certifications in the partner portal- you'll need a minimum of 2 certifications for either Data Engineer or Cloud Architect respectively. 

In the event that you do not yet have certified resources on staff, here are some helpful tips to get your team started:

• • Choose a self-paced training option with QwikLabs or Coursera

  • Review exam material and take practice tests

Practice exams for the 2 different certifications can be found here:

• • Data Engineer

  • Cloud Architect 

Contact Partner Concierge team