Ankur Tyagi for AgentCloud

Posted on Apr 29 • Originally published at agentcloud.dev

Agent Cloud vs Google Cloud Agents

#opensource #openai #webdev #productivity

Introduction

Data has become the new gold mine. Businesses of all sizes are scrambling to extract valuable insights and unlock the potential hidden within their ever-growing data warehouses. Generative AI tools powered by Large Language Models (LLMs) are emerging as powerful tools for such tasks.

Based on large pre-trained data and the context given in the input, these AI marvels can generate responses in the form of text, image, code, and even videos that you can leverage for different needs.

But what if LLMs could also leverage external data sources to provide even more insightful and accurate results instead of only relying on pre-trained data? This is where Retrieval-Augmented Generation (RAG) comes in.

RAG takes LLM capabilities to the next level, allowing them to access and integrate information from external databases and repositories.

Imagine an LLM that can write a compelling product description and dynamically incorporate real-time customer feedback to enhance its accuracy. This is the power of RAG.

AgentCloud is an open-source generative AI platform that offers a built-in RAG pipeline to help you securely talk to your data using your preferred LLM. AgentCloud's built-in RAG pipeline simplifies data integration from over 300 sources, including Google BigQuery, Salesforce, Atlassian Confluence, Zendesk, Airbyte, Drive, SharePoint, and OneDrive.

Oh, By The Way

AgentCloud is a community-powered open-source project so If you find value in our work and wish to fuel our journey so the best way to show your support is by starring us on GitHub ⭐

Here is our public roadmap.

Now, let's discuss Google’s most recent addition, Vertex AI Agent Builder. This suite of tools allows developers of all experience levels to build and deploy generative AI applications. It offers a no-code interface, an open-source library(LangChain) for advanced users, and tools to ensure the AI responses are based on real-world data. With features like document processing and security controls, Vertex AI Agent Builder is an all-in-one solution for creating production-ready generative AI experiences.

This article will compare AgentCloud and Vertex AI agent builders, exploring their functionalities, strengths, and weaknesses to help you choose the most suitable solution for your needs.

Understanding AgentCloud - Talk to Your Data.

AgentCloud is an open-source AI application platform designed to empower you and your businesses to build and use conversational AI tools, like ChatGPT, privately within your organization

AgentCloud facilitates through an advanced built-in RAG as a service that allows you to split, chunk, embed, and retrieve data from over 300 sources. You can also upload your own files in the formats of PDF, docx, txt, xlsx, and CSV.

For databases, you can choose specific tables and columns for data ingestion. Once you have your data, you can define how it's processed. This includes splitting and chunking files and choosing an embedding model. Finally, AgentCloud stores the prepared data securely in a vector database and keeps it fresh through manual, scheduled, or automated updates. With everything set up, you can create an AI agent and interact with your data through a chat-like interface.

Suppose you're in the marketing department, scrambling to meet a deadline for a new marketing campaign. You need some data on customer acquisition costs from last quarter, but you can't remember exactly where it's stored. You fire off a quick message to Sarah in Sales.

A few minutes tick by, and you have yet to hear back. Sarah may be engaged in another task or digging through spreadsheets from last quarter, further delaying you.

With AgentCloud, instead of relying on colleagues' availability, you open the AgentCloud interface. It's like having a chat window directly connected to your company's data. You type your question.

"What were our customer acquisition costs in Q3?"

AgentCloud instantly retrieves the relevant data from your CRM, marketing automation tool, or any other designated source. You see a clear breakdown of the costs, presented in a user-friendly format.

This is the power of AgentCloud.

It eliminates the need for endless email chains and searches. You can chat with your data directly, just like you would with a colleague, unlocking valuable insights and accelerating your decision-making process.

AgentCloud is designed to be LLM-agnostic, meaning you can choose the LLM that best suits your needs.

Here's a breakdown of your options:

Open-source LLMs: Since AgentCloud is open-source, you can integrate various open-source LLMs, such as LLAMA2, Mistral 7B, Hugging Face, or BLOOM.
Cloud-based LLMs: AgentCloud allows you to connect to cloud-based LLMs offered by providers like Open AI, Cohere, and Anthropic Claude. This gives you access to powerful pre-trained models like OpenAI's GPT-3 series, known for their capabilities in text generation and code completion. This is perfect for companies that want to get up and running fast or don't want to manage infrastructure.

While AgentCloud excels at facilitating conversations with your data through a chat interface, its capabilities extend far beyond this. AgentCloud's Multi-agent assistant, powered by Autogen, empowers you to build robust, collaborative teams of AI agents. AutoGen provides a multi-agent conversation framework as a high-level abstraction.

With this framework, you can conveniently build a group of agents and provide them access to functions and data sources. Once provided with a prompt, these agents can securely leverage those data sets and converse with each other to solve tasks.

The power of AgentCloud continues beyond multi-agent collaboration. You can further extend your AI agents' capabilities by building custom tools and functions. These custom Python functions allow your agents to interact seamlessly with any third-party API service. Imagine needing sales data from your CRM system or marketing performance metrics from an external platform.

With AgentCloud, you can build custom functions that securely access these APIs and integrate the retrieved data directly into your agent workflows. This empowers you to create genuinely comprehensive AI solutions that bridge the gap between your internal data and external resources.

Finally, AgentCloud allows you to maintain complete control over your data and ensure chat applications don't share more information than needed. We recommend self-hosting on your computer if you’d like to operate in an ultra-secure environment.

You can deploy the entire platform on your infrastructure and leverage private LLM endpoints. This ensures your data remains completely isolated and LLM providers won't have access to train on it. Furthermore, AgentCloud offers granular access controls at multiple levels. Teams can only access data designated for them, while individual AI agents have restricted access to specific data, functions, and LLM models.

Key AgentCloud Features at a Glance

Talk to data, no coding (RAG as a service) - Build chatbots that chat with your data! No coding is needed; use AgentCloud's built-in RAG (fancy term for "knowledge retrieval") feature. Plus, it uses multiple data sources to keep things accurate.
Conversation management - You can build chatbots that know what they're talking about! AgentCloud uses your connected data sources as a giant knowledge base, so your chatbots can answer questions and act like a supercharged internal search engine for your data.
Data on demand (Data ingestion) - AgentCloud syncs information from all your favorite sources (think Confluence, databases, even PDFs!) into a central hub with a built-in Qdrant vector database. Plus, you can choose how often to refresh the data, keeping it nice and up-to-date.
Teamwork makes the dream work (Multi-agent engine) - You can automate complex tasks with a team of AI agents working together. Think of them as your data dream team each with their specialty, but also able to collaborate to get the job done.
You're in control - You get to decide who gets access to what data with AgentCloud's team and user permissions. Keep things organized and secure.
Your data, your rules - Put your data privacy first. AgentCloud lets you deploy it on your cloud servers, keeping your information securely within your control.

Understanding Google Cloud agents - Vertex AI Agents builder

Vertex AI Agent Builder is one of Google's most recent AI platforms. It allows you to create and deploy generative AI experiences for businesses. Unlike traditional coding, Vertex AI Agent Builder allows you to build agents using natural language instructions. This no-code interface makes it accessible to a wider range of developers, even those without extensive programming experience. You can define your agent's goals, provide step-by-step instructions, and share conversational examples to guide its responses.

Vertex AI Agent Builder allows you to integrate your agents with your data for more accurate and relevant responses. It offers pre-built options like Vertex AI Search for out-of-the-box grounding, or for more control, you can build custom Retrieval-Augmented Generation (RAG) systems using its search component APIs. Additionally, vector search capabilities allow for building even more accurate and valuable embedding-based agents.

With Vertex AI data connectors you can also ingest your data from third-party applications like ServiceNow, Hadoop, Salesforce, and other commonly used enterprise systems.

Complex tasks can be broken down into smaller, more manageable components. Vertex AI Agent builder allows you to create multiple agents, with a "main" agent and supporting "subagents." These subagents can collaborate seamlessly, passing information and collaborating to achieve the overall goal.

Vertex AI Agent builder goes beyond prototyping. It provides tools to refine your initial concepts into production-grade agents. You can monitor agent performance in real time, identify areas for improvement, and use natural language training to enhance responses to specific queries. You can monitor key metrics like usage, latency, safety, and cost to identify potential issues and optimize performance over time.

Vertex AI Agent builder includes built-in security features to ensure your agents meet enterprise scaling needs, compliance, and security standards. It also includes features that allow you to easily manage access and ensure the responsible use of AI models and data.

Key Vertex AI Agent builder features at a Glance.

Ditch the complex coding - Vertex AI Agent builder features an intuitive no-code interface that allows you to design conversational AI agents using plain English. Define your agent's goals, provide step-by-step instructions for achieving them, and share sample conversations to guide its responses.
Flexibility - Vertex AI Agent builder caters to non-technical users through the no-code interface. For experienced coders, LangChain on Vertex AI offers a code-first approach for creating highly customized and powerful agents.
Modular agent architecture - For complex tasks, you can build subagents that work seamlessly together. Imagine a customer service scenario where a primary agent handles initial inquiries. At the same time, a subagent retrieves user purchase history directly from your CRM system, providing a more holistic and personalized experience.
Pre-built grounding systems - Out-of-the-box options like Vertex AI search seamlessly connect your agents to your company data, ensuring responses are anchored in reliable sources.
Customizable RAG — You can use Vertex AI's Search Component APIs to build bespoke RAG systems. These systems empower you to process documents, rank retrieved information based on relevance, and communicate with your own data.
Real-time Monitoring and Optimization - Continuously monitor agent performance, identify areas for improvement, and use natural language training to enhance responses to specific queries. This ensures your agents remain relevant and effective over time.
Enterprise-Grade Security and Compliance - Built-in security features ensure your AI agents operate within the boundaries of your organization's compliance standards.

Comparison between AgentCloud and Vertex AI Agent builder

Both AgentCloud and Vertex AI Agent Builder offer compelling features. But which one is the right fit for your needs? Let's delve into their key functionalities to help you decide.

Focus and flexibility: Agent Cloud specializes in data interaction and conversation building, making it ideal for building chatbots that users can interact with to access information. It also offers a built-in process automation functionality. You can leverage multiple AI agents working together "multi-agents" to automate complex and repetitive business processes.

Vertex AI Agent Builder offers both conversational AI agents and process automation functionalities. This allows you to build a broader range of virtual assistants. You can create customer service chatbots, data analysis tools, or even workflow automation tools using subagents and function calls. While it offers process automation, it might not be as robust as Agent Cloud's multi-agent approach designed for this purpose.

Data integration and grounding: AgentCloud boasts a built-in Retrieval-Augmented Generation (RAG) system as a service, facilitating the creation of data-driven chat applications. You can connect to over 300 data sources and embed information for retrieval through your chat app.

Vertex AI agent builder also provides multiple options for data grounding. You can leverage pre-built systems like Vertex AI Search or build custom RAG systems using Search Component APIs. This grants more significant control over how your agents access and utilize data.

Process automation: AgentCloud excels in automating processes by enabling the use of multiple AI agents working together. This is ideal for streamlining complex workflows and eliminating repetitive tasks.

Vertex AI Agent builder: While it supports process automation, its focus is less pronounced than Agent Cloud. However, it offers functionalities like subagents and function calls, allowing for some level of workflow automation.

Development approach and security: AgentCloud’s open-source nature allows for self-deployment on your preferred cloud infrastructure, offering greater control and potentially enhanced data privacy. However, you'll need to manage the underlying applications yourself.

Vertex AI Agent builder on the other hand operates within Google Cloud's infrastructure, providing a managed service with built-in security features for responsible use of AI models and data. This eliminates the need for in-depth infrastructure management but may not offer the same level of customization as a self-hosted solution.

Openness and scalability: Agent Cloud is LLM-agnostic, meaning you can connect your open-source models or use cloud models from providers like OpenAI or Azure. This offers excellent flexibility but may require additional configuration.

At the time of writing this article, Vertex AI Agent Builder supported Google's latest Gemini models. It integrates seamlessly with other Google Cloud services, facilitating scalability within the Google Cloud ecosystem.

What to Consider When Choosing Between Agent Cloud and Vertex Agent Builder.

If your primary focus is building chatbots for users to interact with and gain insights from your data, AgentCloud excels in this area. Its built-in RAG system and data embedding functionalities streamline data retrieval and conversation management within your chat application.

For complex workflows requiring automation with multiple AI agents working together, Agent Cloud's multi-agent approach is a strong choice. AgentCloud also offers a broader range of data source connections (over 300) and the ability to use your open-source models. This provides greater control over integrating and utilizing data within your AI agents.

If you prioritize open-source solutions and want complete control over deployment on your preferred cloud infrastructure, AgentCloud allows for self-deployment. This can be ideal if your company has strict data privacy requirements.

When your project demands a broader range of virtual assistant capabilities, Vertex AI Agent Builder offers both conversational AI agents and process automation functionalities. This allows you to build a broader spectrum of AI-powered tools.

If you have developers new to AI development, Vertex AI Agent builder's no-code interface simplifies the agent creation process. This can significantly reduce development time and resources. AgentCloud has a similar interface.

Vertex AI Agent Builder operates within Google Cloud's infrastructure, offering built-in security features and seamless integration with other Google Cloud services. If you're comfortable using Google's cloud-based AI models Vertex AI Agent Builder is a strong choice.

Conclusion

AgentCloud and Vertex AI Agent builder (Google Cloud Agents) offer outstanding features that empower you to interact with your data through conversation and automate tasks. While Vertex AI Agent Builder excels in its versatility and ease of use, AgentCloud shines for its focus on data interaction and complex process automation with multi-agent workflows. Ultimately, the choice depends on your specific needs.

For the most control over data integration and the power of multi-agent automation, consider exploring AgentCloud. It offers an open-source approach and a wider range of data source connections, making it ideal for organizations with complex data interaction requirements.

That's it for today. I hope you found this article helpful.

Ready to jump in? our Quickstart guide is the perfect next step to get you going with AgentCloud.