1. About n8n
When I first dove into AI agent development, I was searching for a flexible platform that could handle complex workflows without breaking the bank. That's when n8n caught my attention!
Founded in 2019 by Jan Oberhauser, n8n (pronounced "n-eight-n") emerged as an open-source alternative to established workflow automation tools. Jan's vision was simple yet powerful: create a tool that gives users complete control over their automation while keeping their data private.
With the explosion of LLMs and AI capabilities in recent years, n8n has evolved into a perfect playground for building sophisticated AI agents. The platform now offers native integrations with OpenAI, Anthropic, and other AI providers, making it easier than ever to incorporate intelligence into your workflows.
What makes n8n stand out from Zapier and similar tools?
🔓 Truly open-source: You can self-host n8n and even modify the code to suit your needs
💰 Fair pricing: Their "fair-code" licensing model means affordable options for businesses of all sizes
🛠️ Ultimate flexibility: Complex branching logic and error handling that other platforms struggle with
🔐 Data privacy: Keep sensitive information on your own servers when self-hosting
For my project, these advantages made n8n the clear choice for building an AI agent that could handle Instagram business communications autonomously.
2. My Task: AI Agent to Automate Business Communications in Instagram 🤖
I was eager to build an AI agent and needed a real-life challenge to tackle. The perfect opportunity came through my fiancé Kateryna Baieva's ceramic studio, Osonnya Ceramics, where I help out whenever possible.
2.1. Osonnya Studio and their communication challenges
Like many small creative businesses, Osonnya Studio relies heavily on Instagram for client communications. The majority of their customers reach out through DMs with predictable questions:
Workshop pricing and payment options
Available time slots for upcoming classes
Booking procedures and confirmation
These conversations, while crucial for business, were consuming hours of Kate's time that could be better spent creating or teaching.
2.2. What I wanted to achieve
After analyzing the communication patterns, I defined a clear set of goals for my AI agent:
Handle routine Instagram messages automatically for specific delegated topics
Notify an administrator when conversations require human intervention
Integrate with external data sources (primarily Google Sheets) to access current pricing and availability
Automatically update booking information in Google Sheets when workshops are successfully booked
This narrowed scope gave me a perfect sandbox for experimenting with AI agent development while solving a genuine business problem.
3. Implementation 🛠️
3.1. Architecture
To build an AI communication agent from scratch in Python would require significant development work. But with n8n, the process becomes much more streamlined.
The architecture follows a simple flow:
Customer sends a message via Instagram
System identifies if the topic falls within AI's delegated responsibilities
If yes, the AI agent processes the request using available tools and context
The system generates an appropriate response and sends it back to the customer
All messages are saved to provide better context for future conversations. When a topic falls outside the bot's scope, we notify a human operator (via Telegram in my implementation) and pause automated processing. This architecture allows for seamless handoff between AI and human agents as needed.
3.2. Session Management
Each communication with a customer is defined as a session. A customer can initiate multiple sessions over time, but only one session can be active at any given moment. Think of these as separate conversations about different topics with the same person.
When our system recognizes a message as appropriate for AI handling, we mark the session as "AI" and let our agent manage the entire conversation thread until it ventures outside its zone of responsibility. If the topic shifts beyond the AI's capabilities, we mark the session as "human" and skip automated processing until that session closes.
To implement this session management, I needed a database to track and store conversation states. MongoDB proved ideal for this purpose with its flexible document structure. When a customer message arrives, we first check for an active session with that particular customer. If none exists, we create a new one and classify it as either "human" or "AI" based on message content. All subsequent messages in that conversation thread are stored within the same record to maintain conversation context.
3.3. AI Agent in n8n
The AI Agent node is one of n8n's most powerful building blocks for creating intelligent workflows. This node acts as the brain of your automation, allowing you to connect large language models (like GPT-4 or Claude) directly to your workflow with minimal coding.
What makes the AI Agent node particularly valuable is its ability to maintain conversation context and use tools. You can define custom functions and data sources that your AI can reference when generating responses, such as pulling pricing information from Google Sheets or checking availability in a calendar. The node handles all the complex prompt engineering behind the scenes, letting you focus on what matters - building useful workflows.
For the Osonnya Studio project, I configured the AI Agent with specific instructions about ceramic workshops, pricing structures, and appropriate response styles. By connecting it to our session database and external data sources, the agent can provide accurate, contextual responses that feel natural to customers while maintaining business consistency.
3.4. AI Agent Deployment 🚀
Finally, I needed to find the best place to deploy my Agent to make it live. I could have used n8n cloud for this purpose, but it turned out to be too expensive for a small business like Osonnya Studio—at least 20 euros per month for the starter package.
So I explored alternatives and eventually settled on Railway: for a hobby project of this size, it costs just 5 dollars a month, which fit my budget perfectly.
BTW, to easily deploy my n8n project to Railway I used this nice template.
On top of this base cost, there's the expense of each LLM call to Anthropic's Claude model, which I chose for this project. Since we weren't expecting thousands of messages daily—just tens at most—the API costs remained reasonable. Overall, this deployment strategy provided an excellent balance of performance and cost-effectiveness that I can definitely recommend for similar small-business automation projects! 💯
4. Results and Conclusion 🎉
Building an AI-powered Instagram agent with n8n was an engaging, hands-on exploration into combining low-code automation with modern AI capabilities. Leveraging n8n’s flexibility, seamless LLM integration, and robust session management allowed for rapid development and iterative refinement, ultimately delivering a practical and efficient automation solution tailored specifically to the communication needs of a small creative business.
