In my latest hobbyist project, JapaAdvisorAI, I engineered an agent-driven AI assistant that leverages stateful orchestration, large language models (LLMs), and real-time information retrieval to provide dynamic, high-fidelity immigration guidance.

This project is not just a chatbot—it embodies AI as an autonomous agent, making real-time decisions, adapting its strategy based on user interactions, and leveraging external tools for knowledge augmentation. Below, I outline the technical stack, architectural principles, and agentic AI design that underpin JapaAdvisorAI’s intelligence.

Tech Stack for JapaAdvisorAI: From Frontend to Backend

Building an AI-enabled product involved a whole host of learnings and utilizing various tools and products. I relied on using AI questions and answering tools for code generation and UI generation but spent a good amount of time getting things to work. Below are the different categories of tools used:

Frontend & API: Javascript/Html/Css (UI), FastAPI (backend API).
AI Orchestration: LangChain (prompt management), LangGraph (stateful conversation flow).
LLM Integration: ChatOpenAI (response generation, query refinement).
State & Storage: PostgreSQL (data storage).
Knowledge Retrieval: TavilySearchResults (real-time search), content extraction for summaries.
Cloud & Deployment: Digital Ocean Serverless tier, Docker.
Code generation: Github co-pilot, Galileo UI, ChatGPT and Deep Seek.

Architecting an Agentic AI System with LangGraph

Unlike conventional rule-based chatbots, JapaAdvisorAI employs LangGraph to model a multi-agent system where conversational nodes represent discrete agents responsible for distinct cognitive functions. This modular approach enhances adaptability, ensures context retention, and enables autonomous decision-making within the AI system.

The key components include:

Multi-Agent Workflow with LangGraph’s StateGraph

At the core of JapaAdvisorAI is LangGraph’s StateGraph, which orchestrates the AI’s decision-making process by defining functional agents as graph nodes. This ensures:

Dynamic conversation routing based on user queries
Adaptive state transitions based on contextual understanding
Parallel execution of reasoning and retrieval tasks

Each node in the graph represents a specialized AI function, forming a decomposed agentic system rather than a monolithic model.

Defining Agent Roles and Responsibilities

The AI system consists of distinct functional agents that operate in a coordinated manner:

Preprocessing Agent (Initialization & Context Injection)

preprocess_node: Captures user intent, extracts contextual details, and initializes the state with prior interactions.
Agent Role: Ensures that every user session retains contextual continuity, mitigating information loss between queries.

Query Refinement Agent (Enhancing User Input)

refine_tool: Utilizes prompt engineering to restructure and disambiguate user queries before passing them to the core LLM.
Agent Role: Acts as a pre-LLM filter, ensuring that user questions are structured optimally for precision in AI responses.

Conversational AI Agent (LLM Integration & Response Generation)

LLM backbone: ChatOpenAI API processes refined queries and generates contextual, multi-turn responses.
Agent Role: Dynamically adapts responses based on conversation history and prior decisions in the state graph.

Information Retrieval Agent (Real-Time Search & Augmented Responses)

generate_content_from_link: Extracts and summarizes retrieved documents for inclusion in responses. Uses TavilySearchResults to fetch relevant immigration information from trusted sources.
Agent Role: Functions as an external knowledge augmenter, ensuring that responses remain factually accurate and policy-compliant.

Intent Extraction Agent (Conditional Routing & Adaptive Transitions)

Intent extraction: Implements conditional_edge logic to route user queries dynamically based on complexity and knowledge gaps.
Agent Role: Determines whether to proceed with response generation, follow-up questioning, or external retrieval, ensuring a multi-step reasoning framework.
- Each agent operates independently yet collaboratively, forming a distributed AI system where decisions emerge from collective reasoning rather than a single-pass LLM call.

Key Innovations in the Agentic AI Architecture

Stateful Orchestration with Graph-Based AI: By leveraging LangGraph’s graph-based AI architecture, JapaAdvisorAI transitions from traditional conversational models to a true agentic AI system.StateGraph ensures a structured, adaptive flow where conversations evolve dynamically rather than following static rules. Agents execute concurrently, enabling parallel processing of follow-ups, search queries, and response generation.AI decisions are no longer sequential; they are emergent behaviors resulting from interactions between different nodes in the system.
Multi-Agent Collaboration for Enhanced Decision-Making: JapaAdvisorAI’s agents operate as a distributed decision-making system where each node specializes in a particular task.
1. Autonomous Follow-Up Generation: The Follow-Up Agent analyzes gaps in user input and triggers clarifying questions automatically.
2. Search-Augmented Reasoning: The AI seamlessly determines when it lacks sufficient knowledge and invokes the Retrieval Agent for real-time updates.
3. Adaptive Response Refinement: The Query Refinement Agent continuously optimizes user inputs to maximize response accuracy.
  This approach mirrors real-world expert consultations, where multiple specialists contribute insights to refine answers progressively.
Hybrid AI: LLM Augmentation with External Knowledge
One of the major challenges in AI-driven advisory systems is maintaining factual accuracy in rapidly changing domains like immigration law. To address this, JapaAdvisorAI employs a hybrid AI approach:
- LLM-powered natural language processing for reasoning and conversation
- External search integration for real-time fact-checking
- Decision logic to determine when search augmentation is needed
This ensures that the AI remains accurate, up-to-date, and grounded in real-world data rather than relying on outdated model knowledge.

Lessons Learned: Building the Next Generation of AI Advisors

1. Agentic AI Is Great For Conversational Systems: Traditional chatbots struggle with context retention, adaptability, and complex decision-making. By employing graph-based AI orchestration, JapaAdvisorAI demonstrates that:

AI assistants can dynamically adjust their strategies based on real-time inputs.
Autonomous agents enhance AI reasoning by specializing in distinct tasks.
Multi-agent systems enable emergent behaviors, where responses evolve based on collective agent decisions.

2. The Best AI Advisors Combine Intelligence with Knowledge Augmentation: LLMs are not sufficient on their own—they require:

Structured query refinement to ensure clarity
Automated knowledge retrieval for real-time updates
Graph-based decision-making to route user queries intelligently
This hybrid AI paradigm significantly outperforms traditional chatbot architectures in knowledge-sensitive domains.

3. Modular AI Design Increases Scalability & Maintainability: By decoupling AI functions into agent-based nodes, I ensured that:

New capabilities (e.g., new search APIs, expanded Q&A models) can be added without overhauling the system.
The AI can scale horizontally, distributing different functions across specialized modules.
The logic remains explainable and auditable, improving trust and compliance in high-stakes AI applications.

JapaAdvisorAI Demo

Here is a video demonstration of Japa Advisor AI agent deployed using Digital Ocean and responding to a simple query about documents required for a USA visa from Nigeria.

Final Thoughts: Building AI for Complex Decision-Making

JapaAdvisorAI represents a paradigm shift in AI advisory systems, proving that agentic architectures, knowledge augmentation, and adaptive workflows are the key to next-gen AI assistants. If you want to use JapaAdvisorAI, please contact me.

As a Principal AI Expert, I specialize in:

Designing multi-agent AI systems using LangGraph
Developing LLM-powered advisory tools with real-world augmentation
Architecting AI that blends reasoning, search, and stateful decision-making

If your organization is exploring agentic AI solutions or needs a leader to drive AI innovation, let’s connect!