Architecting a Multi Agent Orchestrator Using Google Cloud Pub Sub and Gemini

Unlock the full potential of enterprise AI by moving beyond the limitations of monolithic models. Learn how decoupled, event-driven architectures can coordinate specialized AI agents to transform rigid pipelines into dynamic, scalable ecosystems.

The Need for Decoupled AI Architectures

As we push the boundaries of what generative AI can achieve, the architectural paradigms we use to deploy these models must evolve. In the early days of LLM integration, the standard approach was straightforward: send a prompt to a single model and wait for a synchronous response. However, as enterprise use cases grow in complexity—requiring diverse skill sets, multi-step reasoning, and integration with various external systems—this single-node approach quickly hits a wall. To build resilient, scalable, and highly capable AI systems, we must look to the proven principles of distributed cloud systems. By transitioning to decoupled AI architectures, we can leverage event-driven orchestration to coordinate multiple specialized AI agents, transforming a rigid pipeline into a dynamic, intelligent ecosystem.

The Bottleneck of Monolithic AI Models

Relying on a single, monolithic AI model to handle every aspect of a complex workflow is akin to asking a single developer to design, code, test, and deploy an entire enterprise application simultaneously. While foundational models like Gemini are incredibly versatile, forcing a single instance to act as the router, the subject matter expert, the data processor, and the final synthesizer introduces severe architectural bottlenecks.

First, there is the issue of latency and context bloat. When a single model is responsible for a multi-step process, the Prompt Engineering for Reliable Autonomous Workspace Agents becomes incredibly convoluted. You are forced to stuff complex instructions, diverse few-shot examples, and massive payloads of context into a single request. This not only eats into your context window limits but significantly degrades inference speed and increases time-to-first-token (TTFT).

Second, monolithic AI architectures suffer from a lack of fault tolerance. If the model hallucinates a step in a complex chain-of-thought process or fails to parse a specific API response, the entire execution fails. Debugging becomes a nightmare because the logic is locked inside a black-box generation process rather than separated into observable, trackable steps.

Finally, scaling becomes highly inefficient. In a monolithic setup, you cannot independently scale the “data extraction” capability without also scaling the “text summarization” capability. This rigid scaling model inevitably leads to wasted compute resources, inflated API costs, and a system that buckles under targeted high-volume requests.

Why Cloud Engineers Should Decouple AI Expertise

As cloud engineers, we already understand the immense value of breaking down monolithic applications into microservices. It is time to apply that exact same philosophy to Artificial Intelligence. By decoupling AI expertise into a Multi-Agent Orchestrator, we assign specific, narrowly defined roles to individual agents. One Gemini agent might be tuned specifically for natural language routing, another strictly for writing SQL queries against BigQuery, and a third for summarizing the final output for the end user.

Decoupling offers several massive advantages for cloud architectures:

• **Independent Scalability: By utilizing an event-driven backbone like Google Cloud Pub/Sub, each agent operates as an independent subscriber. If your system experiences a massive spike in database query requests, Pub/Sub will buffer the messages, and compute platforms like Cloud Run or GKE can auto-scale only the SQL-generating agent to handle the load, leaving the rest of the system untouched.

• Asynchronous Resilience: Decoupling shifts the architecture from synchronous API calls to asynchronous message passing. If an external API goes down, the agent responsible for interacting with it can fail gracefully. Pub/Sub can then utilize dead-letter topics (DLQs) and exponential backoff retries to handle the failure without blocking the rest of the orchestration pipeline.

• Model Optimization and Cost Efficiency: Not every task requires the heavy lifting of your largest foundational model. By decoupling, you can route simpler, high-volume tasks to faster, more cost-effective models like Gemini 1.5 Flash, reserving the massive reasoning capabilities of Gemini 1.5 Pro strictly for complex, high-value tasks.

• Simplified CI/CD and Prompt Versioning: When AI capabilities are isolated into distinct agents, updating the system prompt, adjusting parameters, or upgrading the underlying model version for one specific task carries zero risk of breaking another. You can deploy, test, and roll back individual agents with the exact same CI/CD pipelines you use for traditional microservices.

Ultimately, decoupling AI expertise using Google Cloud Pub/Sub and specialized Gemini agents transforms your AI from a fragile single point of failure into a robust, distributed nervous system capable of handling true enterprise-grade complexity.

Designing the Multi Agent Orchestration System

Transitioning from a single, monolithic Large Language Model (LLM) call to a distributed multi-agent system requires a paradigm shift in how we handle state, routing, and execution. Instead of forcing one model instance to juggle context, persona, and disparate tasks simultaneously, an orchestrated system divides responsibilities among specialized agents. To achieve this on Google Cloud, we rely on an event-driven architecture. This ensures that our agents are decoupled, highly scalable, and resilient to failures or timeouts—crucial factors when dealing with generative AI workloads.

Core Components: Google Cloud Pub/Sub, Cloud Functions, and Gemini API

To build a robust, asynchronous multi-agent orchestrator, we leverage a triad of Google Cloud services, each serving a distinct architectural purpose:

• Google Cloud Pub/Sub (The Nervous System): In a multi-agent setup, agents need to communicate without being tightly coupled. Pub/Sub provides the asynchronous messaging backbone. By using topics and subscriptions, we can route tasks between agents, buffer requests during traffic spikes, and implement robust retry mechanisms if an agent encounters an API rate limit or transient error.

• Cloud Functions (The Compute Layer): We need a lightweight, serverless environment to host the logic for each individual agent. Cloud Functions (or Cloud Run) act as the physical embodiment of our agents. They are triggered directly by incoming Pub/Sub messages, meaning they scale down to zero when idle and scale up massively when a complex, multi-step orchestration is underway.

• Gemini API (The Cognitive Engine): Google’s Gemini models provide the reasoning and generative capabilities. Depending on the agent’s role, we might use Gemini 1.5 Flash for rapid, low-latency routing decisions, or Gemini 1.5 Pro for complex reasoning, deep code analysis, or extensive data synthesis. Gemini’s massive context window and native support for structured JSON output make it exceptionally well-suited for programmatic orchestration.

The Router Agent Pattern Explained

At the heart of our orchestration system sits the Router Agent (sometimes referred to as a Supervisor or Triage Agent). When a user or external system submits a complex prompt, it doesn’t go straight to a worker. Instead, it lands on an initial Pub/Sub topic (e.g., incoming-requests-topic), which triggers the Router Agent.

The Router Agent’s primary responsibility is intent recognition and task delegation. It does not execute the actual work. Instead, it uses a carefully crafted system prompt and the Gemini API to analyze the incoming request and determine which specialized worker agent is best equipped to handle it.

By leveraging Gemini’s “Structured Outputs” (JSON mode) or Function Calling, the Router Agent evaluates the prompt and outputs a routing decision. For example, if a user submits, “Analyze these server logs and write a summary for the marketing team,” the Router Agent’s Gemini call might output a JSON payload identifying two required downstream tasks: log analysis and copywriting. The Router Agent then publishes new, targeted messages to the specific Pub/Sub topics associated with those downstream tasks, effectively fanning out the workload.

Mapping Specialized Worker Agents

Once the Router Agent has delegated the tasks, the Worker Agents take over. Worker agents are narrowly scoped, highly specialized instances of Cloud Functions, each listening to its own dedicated Pub/Sub subscription (e.g., data-analysis-sub, code-review-sub, creative-writing-sub).

Mapping these specialized agents involves three key configurations:

1. Dedicated Pub/Sub Topics: Each worker agent has a specific topic. This allows the Router Agent (or even other worker agents) to invoke them simply by publishing a message to that topic.

2. Tailored System Instructions: Because each worker has a narrow focus, we can optimize their Gemini API calls with highly specific system prompts. The “Data Analyst Agent” is instructed to think logically, output structured data, and ignore creative flair. The “Copywriter Agent” is given brand guidelines, tone-of-voice instructions, and formatting rules.

3. Tool Access: Worker agents can be mapped to specific external tools or APIs. For instance, a “Database Query Agent” might be the only agent in the orchestrator granted IAM permissions to execute BigQuery jobs, while a “Web Search Agent” is equipped with Google Search grounding.

By mapping specialized worker agents in this decoupled manner, you can easily add, update, or remove agents from your orchestrator without touching the rest of the system. If you need a new “Security Audit Agent,” you simply deploy a new Cloud Function, create a new Pub/Sub topic, and update the Router Agent’s prompt to make it aware of this new capability.

Implementing Pub Sub Routing Logic

At the heart of our multi-agent orchestrator lies Google Cloud Pub/Sub. By adopting an event-driven architecture, we decouple the primary orchestrator from the specialized downstream Gemini agents. This asynchronous routing logic ensures that our system remains highly scalable, allowing individual agents to process tasks at their own pace without blocking the main execution thread.

Configuring Topics for Code and Summarization Agents

To effectively route tasks, we need a topic topology that reflects the specialized capabilities of our Gemini agents. Instead of a monolithic queue, we will provision distinct Pub/Sub topics for distinct cognitive tasks. For this architecture, we will configure two primary worker topics: one for the Code Generation Agent and another for the Summarization Agent.

When the Orchestrator evaluates an incoming user request, it determines the intent and publishes a message to the appropriate topic.

You can provision these topics and their corresponding pull or push subscriptions using the Google Cloud CLI. Here is how you set up the infrastructure:

# Create the specialized agent topics

gcloud pubsub topics create agent-code-generation

gcloud pubsub topics create agent-summarization

# Create subscriptions for the agents (assuming push subscriptions to Cloud Run endpoints)

gcloud pubsub subscriptions create sub-agent-code-generation \

--topic=agent-code-generation \

--push-endpoint=https://code-agent-service-hash-uc.a.run.app/process \

--ack-deadline=60

gcloud pubsub subscriptions create sub-agent-summarization \

--topic=agent-summarization \

--push-endpoint=https://summarization-agent-service-hash-uc.a.run.app/process \

--ack-deadline=60

By isolating the traffic, we can scale the underlying compute resources independently. If your application experiences a surge in code generation requests, the agent-code-generation topic and its subscribers will scale up without impacting the latency of the summarization workflows.

Structuring the Event Payload for Gemini

Pub/Sub transmits message payloads as base64-encoded strings, but the underlying data structure must be meticulously designed to provide the Gemini API with everything it needs to execute the prompt. A well-architected payload should include the prompt, contextual history, model configuration, and routing metadata.

We utilize the Return Address pattern by including a reply_to field. This tells the specialized agent where to publish its response once Gemini finishes generating the content, allowing the Orchestrator to aggregate the final output.

Here is an optimal JSON schema for the event payload:

{

"orchestration_metadata": {

"session_id": "req-8f7e-4b2a-91cc",

"correlation_id": "step-2-code-gen",

"reply_to_topic": "orchestrator-aggregation-topic"

},

"gemini_payload": {

"system_instruction": "You are an expert <a href="https://votuduc.com/JSON-to-Video-Automated-Rendering-Engine-p618510">JSON-to-Video Automated Rendering Engine</a> developer. Output only valid, executable code.",

"prompt": "Write a Python script to parse Cloud Storage access logs and extract IP addresses.",

"context": "The logs are formatted as JSONL. Previous steps determined the target bucket is 'gs://prod-access-logs'."

},

"model_config": {

"model_version": "gemini-1.5-pro",

"temperature": 0.2,

"max_output_tokens": 2048

}

}

When the agent’s microservice receives this payload, it deserializes the JSON, maps the gemini_payload and model_config directly to the Building Self Correcting Agentic Workflows with Vertex AI Gemini SDK, and executes the call. The strict schema ensures that no context is lost during the asynchronous handoff.

Ensuring Fault Tolerance with Dead Letter Queues

When orchestrating LLMs, transient failures are inevitable. You might encounter Vertex AI quota limits, context window overflows, or malformed JSON responses that cause the agent’s parsing logic to crash. To prevent these localized errors from creating infinite retry loops or dropping tasks entirely, we must implement Dead Letter Queues (DLQs).

A DLQ is a secondary Pub/Sub topic where messages are routed if they cannot be successfully processed (acknowledged) after a specified number of delivery attempts.

To configure this, we first create the DLQ topic and a corresponding subscription for monitoring or manual intervention:

# Create the Dead Letter Queue topic and subscription

gcloud pubsub topics create agent-code-generation-dlq

gcloud pubsub subscriptions create sub-agent-code-generation-dlq \

--topic=agent-code-generation-dlq

Next, we update our primary agent subscription to route failed messages to the DLQ after 5 unsuccessful attempts. Note: Pub/Sub requires specific IAM permissions to publish to a DLQ and acknowledge the original message.

# Grant Pub/Sub service account permissions to publish to the DLQ

PUBSUB_SA="service-${PROJECT_NUMBER}@gcp-sa-pubsub.iam.gserviceaccount.com"

gcloud pubsub topics add-iam-policy-binding agent-code-generation-dlq \

--member="serviceAccount:$PUBSUB_SA" \

--role="roles/pubsub.publisher"

gcloud pubsub subscriptions add-iam-policy-binding sub-agent-code-generation \

--member="serviceAccount:$PUBSUB_SA" \

--role="roles/pubsub.subscriber"

# Update the subscription with DLQ routing logic

gcloud pubsub subscriptions update sub-agent-code-generation \

--dead-letter-topic=agent-code-generation-dlq \

--max-delivery-attempts=5

With DLQs in place, your multi-agent orchestrator becomes highly resilient. If the Code Agent fails to generate a valid script after five retries, the message is safely parked in the DLQ. You can then configure Cloud Monitoring alerts on the DLQ topic to notify your engineering team, allowing them to inspect the payload, adjust the Gemini prompt, and replay the message without disrupting the rest of the orchestration pipeline.

Building and Deploying the Specialized Agents

With our Pub/Sub messaging backbone in place, it is time to build the muscle of our architecture: the specialized Gemini agents. By decoupling our agents into independent microservices, we ensure that our orchestrator remains lightweight and that each agent can scale independently based on its specific workload.

Google Cloud Functions (2nd Gen) is the ideal compute environment for this. Built on Cloud Run, 2nd Gen functions offer longer execution timeouts (up to 60 minutes), larger instance sizes, and native Eventarc integration with Pub/Sub—perfect for handling the variable latency of Large Language Model (LLM) inference.

Deploying the Code Agent via Cloud Functions

The Code Agent is designed to handle complex programming tasks, such as generating boilerplate, debugging snippets, or refactoring legacy code. Because coding requires deep reasoning and a large context window, we will power this agent using the gemini-1.5-pro model via the Vertex AI SDK.

To deploy this agent, we create a Cloud Function triggered by a specific Pub/Sub topic, for example, topic-code-agent-requests. The function extracts the prompt from the Pub/Sub message, injects a specialized system instruction to define the agent’s persona, and calls the Gemini API.

Here is a conceptual look at the Python implementation for the Code Agent:

import base64

import json

import functions_framework

import vertexai

from vertexai.generative_models import GenerativeModel, Part

# Initialize Vertex AI

vertexai.init(project="your-gcp-project", location="us-central1")

# Define the model with a specialized system instruction

system_instruction = "You are an expert software engineer. Provide clean, efficient, and well-documented code. Always output code in markdown blocks."

model = GenerativeModel(

"gemini-1.5-pro-preview-0409",

system_instruction=[system_instruction]

)

@functions_framework.cloud_event

def process_code_task(cloud_event):

# 1. Decode the Pub/Sub message

pubsub_message = cloud_event.data["message"]

payload = json.loads(base64.b64decode(pubsub_message["data"]).decode("utf-8"))

task_id = pubsub_message["attributes"].get("task_id")

prompt = payload.get("prompt")

# 2. Invoke Gemini

response = model.generate_content(prompt)

# 3. Route the result (handled in the state management section)

handle_callback(task_id, response.text, "code_agent")

You can deploy this function using the Google Cloud CLI, ensuring you bind it to the correct Pub/Sub trigger:

gcloud functions deploy code-agent-function \

--gen2 \

--runtime=python311 \

--region=us-central1 \

--source=. \

--entry-point=process_code_task \

--trigger-topic=topic-code-agent-requests \

--timeout=120s

Deploying the Summarization Agent via Cloud Functions

The Summarization Agent serves a different purpose: digesting large volumes of text, logs, or documents and extracting key insights. Because summarization tasks often prioritize speed and cost-efficiency over complex logical reasoning, this agent is an excellent candidate for the gemini-1.5-flash model.

The deployment methodology mirrors the Code Agent, but it listens to a different topic (topic-summarization-agent-requests) and utilizes a different system prompt.

By isolating the Summarization Agent in its own Cloud Function, you gain granular control over resource allocation. If a massive batch of documents is uploaded to your system, the topic-summarization-agent-requests topic will experience a spike in messages. Cloud Functions will automatically scale out the Summarization Agent instances to process the backlog concurrently, entirely independent of the Code Agent’s infrastructure.

# Summarization Agent Initialization

model = GenerativeModel(

"gemini-1.5-flash-preview-0409",

system_instruction=["You are an expert analyst. Summarize the provided text concisely, highlighting the top 3 key takeaways."]

)

Managing State and Asynchronous Callbacks

Because Pub/Sub is an asynchronous, event-driven service, the central orchestrator does not block or wait for the specialized agents to finish their Gemini API calls. While this prevents bottlenecks, it introduces a new challenge: how do we track the status of a task and retrieve the final output?

To solve this, we must implement a robust state management and asynchronous callback pattern using Firestore and a dedicated Callback Pub/Sub Topic.

Here is how the lifecycle of a single multi-agent request is managed:

1. State Initialization: When the orchestrator decides to delegate a task, it generates a unique task_id (e.g., a UUID). It creates a document in a Firestore collection named AgentTasks with the task_id as the document ID, setting the status to PENDING.

2. **Message Attributes: The orchestrator publishes the task to the appropriate agent’s Pub/Sub topic. Crucially, it includes the task_id in the Pub/Sub message attributes (metadata), rather than just the payload.

3. Agent Processing: The specialized Cloud Function (Code or Summarization) picks up the message, extracts the task_id, and processes the prompt using Gemini.

4. **The Callback: Once Gemini returns the generated content, the agent does not return the data via an HTTP response. Instead, it executes a callback routine. It publishes the final result to a centralized topic-agent-callbacks Pub/Sub topic.

5. State Resolution: A dedicated “State Manager” Cloud Function listens to topic-agent-callbacks. When it receives a result, it updates the corresponding Firestore document to COMPLETED and writes the Gemini output to the database.

This architecture guarantees idempotency and fault tolerance. If a Cloud Function crashes mid-generation due to an API timeout, Pub/Sub will automatically retry the message. Because the state is centrally managed in Firestore, the orchestrator can easily poll the database or use Firestore real-time listeners to notify the end-user the exact moment their specialized agent has completed its work.

Scaling and Monitoring for SREs

When transitioning a multi-agent orchestrator from a proof-of-concept to a production-grade system, the Site Reliability Engineering (SRE) perspective becomes paramount. Combining the asynchronous, decoupled nature of Google Cloud Pub/Sub with the compute-intensive, non-deterministic nature of Large Language Models (LLMs) like Gemini introduces unique operational challenges. Your system is no longer just scaling stateless microservices; it is managing stateful, token-heavy workflows that can easily bottleneck if left unchecked. For SREs, the focus shifts to ensuring predictable performance, robust error handling, and deep visibility into the asynchronous event mesh.

Observability and Tracing Across Agent Workflows

In a multi-agent architecture, a single user request might trigger a cascade of events: Agent A acts as a router, publishing a message to a Pub/Sub topic; Agent B picks it up, queries Gemini, and publishes the result; Agent C synthesizes the final response. Without proper tracing, this asynchronous dance quickly becomes an operational black box. If a workflow stalls, you need to know exactly which agent failed or if a message is languishing in a Pub/Sub backlog.

To achieve end-to-end visibility, you must implement distributed tracing using Cloud Trace and OpenTelemetry. The golden rule for asynchronous observability is Context Propagation. When an agent publishes a message to Pub/Sub, it should inject the current trace context (Trace ID and Span ID) directly into the Pub/Sub message attributes—not the message payload.

When the downstream agent consumes the message, it extracts this context, allowing Cloud Trace to stitch together the disparate execution spans into a single, unified waterfall graph.

Furthermore, you should establish strict Service Level Indicators (SLIs) for your agent workflows. Key metrics to monitor in Google Cloud Observability include:

• Pub/Sub Oldest Unacked Message Age: To detect stalled agent queues.

• Agent Processing Latency: The time taken from pulling a message to publishing the next step.

• Gemini API Latency: Tracking the response times of the Vertex AI endpoints, segmented by model (e.g., gemini-1.5-pro vs. gemini-1.5-flash).

By enforcing structured logging (JSON) across all agent environments (whether running on Cloud Run or GKE) and injecting the trace_id into every log entry, SREs can seamlessly pivot from a slow trace directly to the exact logs generated by the agent and the Gemini API during that specific execution.

Optimizing Gemini API Quotas and Costs

LLM orchestration at scale can quickly consume API quotas and inflate cloud spend if not meticulously managed. Vertex AI enforces strict quotas on Requests Per Minute (RPM) and Tokens Per Minute (TPM). In a highly concurrent Pub/Sub architecture, a sudden spike in messages can easily cause your agents to overwhelm these limits, resulting in 429 Too Many Requests errors.

To build a resilient system, SREs must leverage Pub/Sub’s native flow control mechanisms to protect the Gemini API. By configuring Subscriber Flow Control, you can limit the maximum number of outstanding messages or bytes an agent processes simultaneously, effectively acting as a shock absorber for Vertex AI quotas.

If rate limits are hit, you should rely on Pub/Sub’s built-in Exponential Backoff for message retries. However, SREs must also configure Dead Letter Topics (DLTs). If an agent repeatedly fails to process a prompt due to persistent quota issues or malformed context, the message should be routed to a DLT after a defined number of delivery attempts. This prevents “poison pill” messages from infinitely consuming compute cycles and API tokens.

From a cost optimization standpoint, SREs and Cloud Architects should implement the following strategies:

1. Intelligent Model Routing: Not every task requires the heavy lifting of gemini-1.5-pro. Use the much faster and cheaper gemini-1.5-flash for intermediary agents handling simple classification, routing, or summarization tasks.

2. Vertex AI Context Caching: If your agents frequently rely on large, static system instructions or reference documents (like massive codebases or policy manuals), utilize Vertex AI Context Caching. This drastically reduces the input token costs and latency for repetitive multi-agent workflows.

3. Proactive Quota Monitoring: Create custom dashboards in Cloud Monitoring to track aiplatform.googleapis.com/generate_content/requests and token consumption. Set up Alerting Policies to notify the SRE team via PagerDuty or Slack when token usage reaches 80% of the allocated quota, allowing for preemptive quota increase requests before the multi-agent orchestrator degrades.

Conclusion and Next Steps

Architecting a multi-agent orchestrator is no longer just a theoretical exercise; it is a practical necessity for enterprises looking to scale complex AI workflows. By leveraging the asynchronous, event-driven power of Google Cloud Pub/Sub alongside the advanced reasoning capabilities of Gemini, we have outlined a system that is resilient, highly scalable, and capable of handling intricate, multi-step tasks. This decoupled architecture ensures that as your business logic grows, your infrastructure will not become a bottleneck. However, deploying the initial orchestrator is merely the first milestone in your AI journey.

Future Proofing Your AI Infrastructure

The true power of an event-driven AI architecture lies in its adaptability. As the landscape of generative AI evolves at breakneck speed, your infrastructure must be able to pivot without requiring ground-up rewrites. Because our Pub/Sub backbone inherently decouples the message producers (user inputs, system triggers) from the consumers (our Gemini-powered agents), you can seamlessly introduce new, specialized agents into the ecosystem. Want to add an agent dedicated solely to code review, or one that cross-references outputs with enterprise data in BigQuery? You simply deploy a new Cloud Run service and subscribe it to the relevant topic.

Furthermore, this architecture makes model lifecycle management frictionless. As Google releases new iterations of the Gemini family on Vertex AI, you can route specific Pub/Sub messages to newer models for A/B testing or gradual rollouts, ensuring zero downtime. To truly future-proof this setup, you should also integrate Google Cloud’s operations suite. Utilizing Cloud Monitoring and Cloud Trace will provide deep observability across your agent network, ensuring you can track the lifecycle, latency, and cost of every prompt and response as your orchestrator scales globally.

Book a GDE Discovery Call with Vo Tu Duc

Navigating the complexities of Cloud Engineering, Automatically create new folders in Google Drive, generate templates in new folders, fill out text automatically in new files, and save info in Google Sheets integrations, and advanced AI orchestration can be daunting. Whether you are looking to validate your current architecture, optimize your Google Cloud infrastructure, or explore custom Gemini implementations tailored to your specific enterprise needs, expert guidance is invaluable.

Take the next step in your AI journey by booking a one-on-one discovery call with Vo Tu Duc, a recognized Google Developer Expert (GDE). During this session, you can dive deep into your specific use cases, discuss advanced deployment strategies for your multi-agent systems, and learn how to maximize the performance and ROI of your GCP environment. Do not leave your AI architecture to chance—connect with a GDE to ensure your systems are built to scale, remain secure, and perform at the highest possible level.