Building a Production-Grade Conversational AI Agent (Part 1)
Discover how to create a robust AI agent and what technology choices we carefully selected
Introduction
In this first part of this blog series, we introduce our conversational AI agent, outline the challenges in today's data-driven world, and explain our technology choices.
In today's fast-paced AI and data-driven world, the demand for scalable, efficient, and flexible infrastructure has never been higher. As organizations strive to deploy AI models and data-intensive applications at scale, Cloud-Native technologies have emerged as a game-changer that allows optimized cost and performance.
At Ingenimax we are developing the first Platform Engineer AI Agent, designed to help organizations scale and manage their Cloud-Native and Cloud-Native AI infrastructure. Our mission is to eliminate the need for specialized expertise while reducing reliance on third-party tools, services, vendors, and complex workflows.
Our fleet of AI agents is built to handle day-to-day platform engineering and MLOps tasks, operating in either human-in-the-loop or fully autonomous modes.
In this series, I'll walk you through the process of building a multi-tenant conversational AI agent using Golang, LangChain, Weaviate, and OpenAI, making it capable of retrieving contextual memory and enhancing responses with vector search capabilities. It supports multiple threads of conversation, multi-tendency, streaming responses, and memory persistence for context-rich interactions. Let's dive in!
To build scalable AI agents, we carefully selected technologies that provide high performance, flexibility, and Cloud-Native compatibility. Here’s a breakdown of our key choices and why we chose them:
Go (Golang) as the primary programming language for this project instead of Python or JavaScript for several reasons:
- High Performance & Concurrency – Go’s lightweight goroutines enable efficient parallel processing, making it ideal for handling multiple concurrent users and long-running tasks.
- Cloud-Native & Kubernetes-Friendly – Go is widely used in Cloud-Native applications and integrates well with Cloud-Native Computing Foundation (CNCF) ecosystems.
- Faster Execution Than Python – While Python is popular in AI/ML development, Go provides significantly better performance for backend and API services.
- Strong Standard Library – Go’s built-in support for networking, concurrency, and JSON handling simplifies API development.
Python is a great choice for prototyping/developing AI models, but Go is the better fit for a production-grade, high-performance conversational agent.
For building the REST API, we chose Echo, a lightweight and high-performance web framework for Go. Some benefits:
- Minimal & Fast – Echo is optimized for speed and minimal memory usage.
- Middleware Support – Built-in middleware for logging, authentication, and request handling.
- Easy to Use – Its API is simple and provides clean request/response handling.
- Scalability – It supports WebSockets and streaming, which are essential for real-time AI responses.
- JSON Handling – Echo provides optimized JSON marshalling for fast API responses.
Compared to Gin (100% opinionated), which is another popular Go framework, Echo provides better request lifecycle management and built-in middleware support, making it ideal for multi-tenant AI applications.
LangChainGo is the Go implementation of LangChain, a framework for building LLM-powered applications. Some benefits:
- Modular & Extensible – Provides built-in chains, tools, and memory management for AI workflows.
- Integrates with OpenAI – Makes it easy to connect to GPT models.
- Supports Memory for Conversations – Essential for multi-turn interactions.
- Lightweight Compared to Python’s LangChain – Optimized for fast execution and Cloud-Native environments.
Using LangChainGo allows us to abstract AI logic into reusable chains while ensuring scalability and performance.
For the LLM backend, we selected gpt-4o-mini, a lightweight yet powerful model.
- Faster & Cheaper than gpt-4o – Provides high-quality responses at lower latency and cost.
- Optimized for Conversational AI – Handles context retention and complex queries effectively.
- Supports Function Calling & Tool Use – Enables API calls, calculations, and structured responses.
- Natural Language Understanding – Well performs in coherence and relevance.
- Easy Integration – Works seamlessly with LangChainGo.
Although there are other good options, gpt-4o-mini is a good choice for production reliability, speed, accuracy and cost.
Storing and Retrieving long-term conversational memory and documents, we chose Weaviate, an open-source vector database.
- Fast Semantic Search – Weaviate stores chat history as vector embeddings, enabling efficient context retrieval.
- Go-Native API – Unlike alternatives like Chroma or Pinecone, Weaviate has first-class Go SDK support.
- Kubernetes Integration & part of Cloud-Native Computing Foundation (CNCF) – Designed for Kubernetes deployments with horizontal scalability.
- Multi-Tenancy Support – Essential for managing multiple users and organizations in our AI agent.
- Modular & Extensible – Allows us to fine-tune indexing and improve response relevance.
- Open-Source, Hosted & Self-Hosted – Unlike Pinecone, Weaviate can be easily self-hosted on Kubernetes, reducing vendor lock-in.
Weaviate is the best choice because it aligns perfectly with our Cloud-Native, Go-based infrastructure while providing high-performance vector search.
Technology Choices
OpenAI gpt-4o-mini
- Faster & Cheaper than gpt-4o
- Optimized for Conversational AI
- Supports Function Calling & Tool Use
Weaviate
- Fast Semantic Search
- Go-Native API
- Kubernetes Integration & part of CNCF
- Multi-Tenancy Support
To summarize - this stack allows us to build a production-grade AI Agents that seamlessly integrates into modern cloud infrastructure.
Next Steps
1
Core Concepts
In the next part, we'll dive into the core concepts that power our AI agent.
2
Memory Management
We'll explore how memory management works in our AI agent.
3
Conversation Threads
Learn about handling multiple conversation threads efficiently.
4
Streaming Responses
Discover how we implement streaming responses for real-time interactions.
These elements work together to create context-rich, multi-tenant interactions in our production-grade AI agent.