Building an AI Agent System in the Cloud using DigitalOcean’s GenAI Platform
Overview
Small businesses need effective marketing strategies just like larger enterprises, but they often operate with tight budgets. After covering expenses such as inventory, accounting, software subscriptions, and payroll, marketing budgets can be extremely limited. To address this challenge, I developed an Agentic AI Marketing Application that functions like a digital marketing agency for small businesses.
The application features an AI-powered Marketing Manager that takes requests from business owners and delegates tasks to specialized AI agents. These agents include an SEO Specialist and an Email Marketing Specialist, ensuring that marketing tasks are handled efficiently without human intervention. This proof of concept (POC) aims to demonstrate cost-efficient AI-powered marketing teams in a cloud environment.
Requirements
I’ve learned from past experience NOT to jump in and start building right away. I needed to understand the business case and objectives first. Here’s a short list of the requirements:
Business Requirements
I needed an Agentic AI system designed to include a Marketing Manager AI Agent responsible for delegating tasks to specialized agents, such as an SEO Specialist and an Email Marketing Specialist.
Business Owners should be able to communicate with the Marketing Manager Agent ONLY.
The Marketing Manager Agent ensures that the business owner’s requests are properly managed and assigned to the appropriate Specialist AI agent.
The AI agents should be restricted to responding only to digital marketing-related inquiries within their domain expertise, preventing misinformation or irrelevant response. In other words, I wanted to prevent the system from hallucinating.
Technical Requirements
Leverage a cloud platform to build all components of the application.
The system needed to incorporate a Vector Database and Retrieval-Augmented Generation (RAG) for efficient knowledge storage and retrieval.
A fully managed GPU-powered solution should handle AI model processing cost-effectively, while agent routing mechanisms should ensure that marketing tasks were properly delegated.
Sensitive data protection should be built-in to the application allowing for scanning for inappropriate content. Mechanisms should be put in place to override agent outputs when necessary, enhancing security and ethical compliance.
After evaluating the use case and requirements, I decided to leverage DigitalOcean’s GenAI Platform to design and build out my AI Application.
Data Sources
To build a reliable Agentic AI Marketing Application, multiple data sources were utilized to enhance its knowledge base and improve response accuracy. A GenAI web crawler was employed to extract relevant data from a personal website, ensuring that the AI agents had up-to-date information for helping business owners with their digital marketing strategies. Additionally, I leverage DigitalOcean’s object storage product, Spaces, to store a PDF with more relevant information to pull from.
Furthermore, an Open Search vector database solution played a crucial role in enabling Retrieval-Augmented Generation (RAG). By storing and retrieving vector embeddings of my knowledge base data, the AI system could efficiently fetch contextually relevant information in real-time. This approach improved the AI system’s ability to generate high-quality responses.
Model Selection
The AI system's core intelligence is powered by Large Language Models (LLMs). For this proof of concept, two models were evaluated: Llama 3.3 Instruct (70B) and DeepSeek R1 Distill Llama 70B. Since this was an initial exploration, no specific preference was given between the two. The primary goal was to assess their respective capabilities in handling digital marketing queries and optimizing responses through fine-tuning. Future iterations of this system may involve collaboration with my fellow AI Architects friends from Go Cloud Careers to further refine model selection based on performance benchmarks and cost-effectiveness. We’ll see :).
Security & Ethics
As the AI system interacts with business owners and handles potentially sensitive information, implementing strong security and ethical safeguards was a top priority. I implemented a solution to regulate access to the AI system, preventing unauthorized interactions to underlying child agents and data. To mitigate risks associated with AI-generated advice, guardrails were put in place to filter and restrict responses in sensitive categories such as violence, hate speech, criminal planning, and regulated substances. This ensures that the AI does not generate or propagate harmful or unethical content.
Multiple guardrails and layers of data protection mechanisms were integrated into the DigitalOcean’s GenAI Platform allowing me to build a Responsible AI system. I believe they’ve partnered with Microsoft, NVIDIA, and Meta for this functionality.
Microsoft Presidio was leveraged to scan AI-generated responses for personally identifiable information (PII) and redact any sensitive data before it reached the user. NVidia’s NeMo Guardrails were deployed to prevent jailbreaking attempts, ensuring that malicious actors could not manipulate the AI into producing harmful outputs. Lastly, Meta’s Llama Guard was implemented to enhance privacy by anonymizing sensitive data, further aligning the system with ethical AI principles and compliance requirements. These combined measures help maintain a responsible and trustworthy Agentic Digital Marketing AI Application.
User Testing
To validate the system's effectiveness and reliability, I’ve done some testing across multiple aspects of the AI Marketing Application.
API testing using Postman was performed to verify connections between agents were routing correctly. Additionally, prompt engineering was used to refine agent instructions, enabling more precise responses tailored to digital marketing tasks.
I did notice delayed responses at times when prompting the systems, especially while testing DeepSeek’s LLM. The latency could have been from me not leveraging components within the same Region. Overall, these testing procedures helped enhance system performance, refine AI-generated Marketing advice, and ensure that the AI agents operated efficiently within defined parameters.
System Optimization
To maximize the efficiency of my AI Marketing Application, multiple optimization techniques were implemented. Fine-tuning model parameters, such as reducing Top-p, temperature, max tokens, and k-value, allowed the system to generate more precise and contextually relevant responses. Retrieval-Augmented Generation (RAG) was implemented to enhance the system’s ability to retrieve relevant information dynamically, reducing hallucinations and improving overall response accuracy. Prompt engineering played a crucial role in refining interactions, ensuring that AI agents followed structured guidelines while generating high-quality content.
If I decide to release this system to the public, I’ll need to reach out to an AI Engineering resource to assist with optimizing system performance.
All in all, these optimizations will ensure that the AI Marketing Application gives high-value insights to business owners without excessive computational overhead.
Noteworthy Architecture Components That Were Left Out
While this proof of concept prioritized core AI functionalities, several critical components were intentionally left out but I’d like to acknowledge them for future enhancements. High availability and disaster recovery (HA/DR) were not implemented at this stage, as the focus was on validating the system’s marketing automation capabilities. Advanced security features, such as Identity and Access Management (IAM), firewalls, and API token security, were also omitted, though they will be crucial for securing production-ready deployments.
Monitoring and observability tools, including billing alerts and system observability mechanisms, were not integrated into the initial build. These features would provide greater transparency into system performance and cost management.
Future iterations of this AI Marketing Application will incorporate these advanced capabilities to improve system security, monitoring, and scalability. As the project evolves, adding these components will ensure a more robust, secure, and production-ready AI-powered marketing agency for small businesses.
Conclusion
This proof of concept demonstrates the potential of AI Agents in the context of Digital Marketing, offering a scalable and cost-effective solution for small businesses. By integrating AI Agents, grounded with SEO and email marketing knowledge into everyday business operations, the system allows for a small business owner to have expert level assistance from a full digital marketing team.
Looking ahead, future enhancements will focus on automation, improved security, and better resource optimization. Features such as automated blog and email content generation, cost management strategies, and a more intuitive front-end interface will further refine the system’s capabilities. Implementing multi-region cloud provisioning and robust IAM controls will enhance security and reliability, ensuring a production-ready AI solution.