Building Intelligent Feedback Systems: A Deep Dive into Conditional Agentic Workflows with…

A technical guide details how developers can build automated customer review triage systems by combining LangGraph, LangChain, Groq, and Pydantic with Meta's LLaMA 3 model, specifically llama-3.3-70b-versatile. The guide argues that standard stateless, linear LLM calls fall short for complex business processes, and that LangGraph's graph-based, stateful architecture enables conditional routing — sending positive reviews to a simple thank-you generator and negative reviews through a diagnostic protocol to determine urgency, tone, and issue type. It also notes that raw unstructured LLM output creates parsing problems for software engineering, motivating the use of Pydantic for strict output formatting.

The landscape of Artificial Intelligence has shifted dramatically over the past couple of years. We are no longer simply chatting with isolated Large Language Models LLMs to generate text or summarize documents. Instead, the industry has aggressively moved toward Agentic Workflows, systems where LLMs act as the reasoning engine within a structured, multi-step process, capable of making decisions, routing information, and executing tasks autonomously. To build these robust systems, developers need tools that can manage complex control flows, maintain state across multiple interactions, and ensure that the outputs from the LLM are predictable and strictly formatted. This brings us to the modern AI stack demonstrated in this guide: LangGraph , LangChain , Groq , and Pydantic . In this comprehensive blog post, we will explore every theoretical concept required to understand how to build a fully automated, intelligent customer review triage system. When LLMs first became widely accessible, the standard interaction model was a direct query-response loop. A user inputs a prompt, and the model outputs a response. While powerful for simple tasks like drafting an email or explaining a concept, this paradigm falls short for complex business processes. A standard LLM call is stateless and linear . It does not possess a memory of past interactions unless explicitly provided in the prompt, and it cannot easily route its own output to different tools based on conditional logic without external scaffolding. Enter the Agentic Workflow . In an agentic workflow, the LLM is not just a text generator; it is a decision-maker. It is integrated into a larger architectural framework that allows it to: In our specific use case: processing customer reviews, a simple prompt might just ask the LLM to write a reply. But an agentic workflow allows the system to first read the review, mathematically determine its sentiment, route positive reviews to a simple “thank you” generator, and route negative reviews through a complex diagnostic protocol to determine the urgency, tone, and specific issue type before finally drafting a highly tailored empathetic response. At the core of this system is the Large Language Model. The demo utilizes the LLaMA 3 family of models, specifically llama-3.3-70b-versatile. To understand why this model is chosen, we must understand its parameters and architecture: LangChain is an open-source framework designed to simplify the creation of applications using large language models. Before LangChain, developers had to write custom API wrappers, manage complex prompt templates mathematically, and write extensive regex regular expressions to parse the output from LLMs. LangChain provides standardized abstractions for: However, standard LangChain often utilizing LCEL — LangChain Expression Language is inherently designed for linear chains A goes to B goes to C . It struggles with complex, cyclical workflows, loops, and branching conditional logic. This limitation birthed LangGraph. To understand the demo, we must understand the concept of a Finite State Machine FSM and Directed Graphs . In computer science, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense “related.” The objects are called nodes or vertices , and the relationships are called edges . LangGraph is an extension of LangChain specifically built for creating stateful, multi-actor applications with LLMs. It models workflows as graphs. One of the most notoriously difficult aspects of working with LLMs is that their natural output is raw, unstructured text. If we ask an LLM to “Diagnose this review and give me the tone and urgency,” it might reply: This variability is a nightmare for software engineering. If we are trying to write a Python script that automatically flags “high” urgency reviews for immediate human intervention, we cannot rely on regex to parse unpredictable conversational text. We need guaranteed, structured data — like a JSON object. This is where Pydantic comes in. Pydantic is a data validation library for Python. It allows developers to define strict data schemas using standard Python type hints. In Pydantic, we create a BaseModel and define exactly what fields we expect, what data types they must be, and even restrict the allowed values using Literal. Let us synthesize these theories into the architecture of the provided demo. The goal is automated Customer Service Triage. This architecture ensures that cheap, simple tasks positive reviews are handled quickly in one step, while complex, sensitive tasks negative reviews are broken down into logical, analytical steps before a final response is generated. We will use LangGraph to orchestrate a dynamic workflow. We start with a customer review, use an LLM to extract its sentiment via structured output, and then use a conditional edge to route the graph to entirely different specialized responder nodes. Here is the cell-by-cell breakdown of the code in Microsoft Fabric Notebook https://www.microsoft.com/en-in/microsoft-fabric . pip install -q langgraph langchain-groq python-dotenv typing-extensions Import necessary librariesfrom langgraph.graph import StateGraph, START, ENDfrom langchain groq import ChatGroqfrom typing import TypedDict, Literalfrom pydantic import BaseModel, Fieldimport operatorfrom IPython.display import Image Initialize the Groq modelmodel = ChatGroq model='llama-3.3-70b-versatile', temperature=0 First, we install our core dependencies. The langgraph library acts as the primary framework we use to build cyclical, stateful agent architectures, while langchain-groq serves as the integration package connecting us to Groq's cloud infrastructure. Groq uses specialized LPU Language Processing Unit hardware to serve LLMs at blazing-fast speeds, which is essential for multi-agent workflows where multiple LLM calls happen simultaneously. We import BaseModel and Field from Pydantic, which are critical for forcing the LLM to reply in a strict data format rather than rambling text. We also import TypedDict and Literal from the typing module to tightly structure our variables and memory limits. Finally, we instantiate our "Brain": the llama-3.3-70b-versatile model via Groq. Crucially, we set the temperature to 0 because we want highly deterministic, analytical categorization of our data, not creative fiction. class SentimentSchema BaseModel : sentiment: Literal "positive", "negative" = Field description='Sentiment of the review' class DiagnosisSchema BaseModel : issue type: Literal "UX", "Performance", "Bug", "Support", "Other" = Field description='The category of issue mentioned in the review' tone: Literal "angry", "frustrated", "disappointed", "calm" = Field description='The emotional tone expressed by the user' urgency: Literal "low", "medium", "high" = Field description='How urgent or critical the issue appears to be' Groq supports with structured output using tool calling under the hoodstructured model = model.with structured output SentimentSchema structured model2 = model.with structured output DiagnosisSchema class ReviewState TypedDict : review: str sentiment: Literal "positive", "negative" diagnosis: dict response: strdef find sentiment state: ReviewState : prompt = f'For the following review find out the sentiment \n {state "review" }' sentiment = structured model.invoke prompt .sentiment return {'sentiment': sentiment}def check sentiment state: ReviewState - Literal "positive response", "run diagnosis" : if state 'sentiment' == 'positive': return 'positive response' else: return 'run diagnosis'def positive response state: ReviewState : prompt = f"""Write a warm thank-you message in response to this review: \n\n\"{state 'review' }\"\nAlso, kindly ask the user to leave feedback on our website.""" response = model.invoke prompt .content return {'response': response}def run diagnosis state: ReviewState : prompt = f"""Diagnose this negative review:\n\n{state 'review' }\nReturn issue type, tone, and urgency.""" response = structured model2.invoke prompt return {'diagnosis': response.model dump }def negative response state: ReviewState : diagnosis = state 'diagnosis' prompt = f"""You are a support assistant. The user had a '{diagnosis 'issue type' }' issue, sounded '{diagnosis 'tone' }', and marked urgency as '{diagnosis 'urgency' }'. Write an empathetic, helpful resolution message.""" response = model.invoke prompt .content return {'response': response} This cell is the architectural marvel of our application, establishing how our agents think, remember, and act. We need our LLM to act as a strict data parser, so we define a SentimentSchema that expects exactly one field for sentiment, using Literal "positive", "negative" to tell the AI that it is only allowed to choose between those two exact strings. For negative reviews, we force the AI to output three specific fields issue type, tone, and urgency via the DiagnosisSchema , and we wrap our Groq LLM in these strict rules using the .with structured output method. We then define our agent's Short-Term Memory scratchpad to dictate exactly what our graph will remember by creating a ReviewState class that inherits from TypedDict. Next, we define functions to represent our specialized, narrow-focus agents : find sentiment reads the raw review and updates the sentiment key; positive response crafts a warm thank-you note using rich, unstructured conversational text; run diagnosis uses structured model2 to break down exactly why the user is mad and saves this rich metadata; and negative response reads the rich diagnosis context to craft a highly tailored, empathetic resolution message. Finally, check sentiment acts as our Python routing logic function. It looks at the sentiment saved in the state , returning 'positive response' if positive or 'run diagnosis' if negative, effectively acting as the switch tracks on our railway. Build Graphgraph = StateGraph ReviewState graph.add node 'find sentiment', find sentiment graph.add node 'positive response', positive response graph.add node 'run diagnosis', run diagnosis graph.add node 'negative response', negative response graph.add edge START, 'find sentiment' graph.add conditional edges 'find sentiment', check sentiment graph.add edge 'positive response', END graph.add edge 'run diagnosis', 'negative response' graph.add edge 'negative response', END workflow = graph.compile Display the graphImage workflow.get graph .draw mermaid png We have our workers nodes and our memory state , but right now, they are just isolated functions until this cell acts as the choreographer. We initialize the StateGraph and physically bind our ReviewState memory structure to it, meaning every node in this graph will now share this exact scratchpad. We use add node to tell LangGraph about our Python functions, and then we draw the edges, ensuring that the absolute first step is always analyzing the sentiment via graph.add edge START, 'find sentiment' . The line graph.add conditional edges 'find sentiment', check sentiment is revolutionary, telling LangGraph that once find sentiment is done, it should not blindly go to the next node; instead, it runs the check sentiment function and navigates to whichever node name that function returns. If routed to positive response, the graph ends; if routed to run diagnosis, the graph passes data to negative response, and then ends. Finally, workflow.compile fuses these rules into an executable application. {'review': 'I’ve been trying to log in for over an hour now, and the app keeps freezing on the authentication screen. I even tried reinstalling it, but no luck. This kind of bug is unacceptable, especially when it affects basic functionality.', 'sentiment': 'negative', 'diagnosis': {'issue type': 'Bug', 'tone': 'angry', 'urgency': 'high'}, 'response': "I'm so sorry to hear that you're experiencing a bug issue and that it's causing frustration for you. I can imagine how annoying it must be, and I'm here to help resolve the problem as quickly as possible.\n\nI've marked your issue as high priority, and I'm working on it immediately. I want to assure you that I'm committed to finding a solution and getting you back up and running smoothly.\n\nTo better understand the issue, could you please provide me with more details about the bug you're experiencing? This will help me to investigate and troubleshoot the problem more efficiently. Please include any error messages you've seen, the steps you took leading up to the issue, and any other relevant information.\n\nI appreciate your patience and cooperation, and I'm confident that we can resolve this issue together. If there's anything I can do to prevent similar issues in the future, I'll make sure to pass on your feedback to our development team.\n\nYou can expect a follow-up from me within the next insert timeframe, e.g., 30 minutes with an update on the status of your issue. If you have any further questions or concerns, please don't hesitate to reach out.\n\nThank you for bringing this to my attention, and I look forward to resolving the issue for you soon."} Run the workflowinitial state = { 'review': """I’ve been trying to log in for over an hour now, and the app keeps freezing on the authentication screen. I even tried reinstalling it, but no luck. This kind of bug is unacceptable, especially when it affects basic functionality."""}final state = workflow.invoke initial state print final state This is the ignition switch where we load a highly critical user review into the initial state dictionary. By calling workflow.invoke initial state , the system springs into action. First, find sentiment reads the text and structurally outputs "negative". The conditional edge evaluates "negative" and routes the flow away from the happy path, sending it to run diagnosis. Next, run diagnosis deeply analyzes the text, determining the issue is a "Bug", the tone is "frustrated", and the urgency is "high", saving this to memory. Then, negative response takes this exact context and crafts a deeply empathetic apology tailored to a high-urgency authentication bug. The resulting output printed to our terminal will be a highly structured, deeply analyzed evaluation. We are no longer programming software by writing explicit lines of hardcoded logic; we are orchestrating intelligence by defining goals, providing toolsets, and designing cognitive architectures. The transition from standard prompting to agentic graphs is an evolution from software as a tool to software as an entity. By mastering state management, structured JSON outputs, and conditional routing in LangGraph, we are learning the intricate dance of memory and planning required to build the autonomous synthetic workforces of the future. This specific demo proves that AI no longer has to treat all inputs the same. By granting an AI the ability to structure data and route its own execution path, we transition from generating text to fundamentally solving business logic autonomously. Hey, I am Sandip Palit , from Kolkata, India. I love to explore what’s new in the Data Science space and share it with the community. I am a Fabric Super User , and in this Agentic AI using Microsoft Fabric Playlist, I will share my learnings and hands-on projects on Agentic AI. Thank You for reading this article. Please feel free to share your thoughts in the comments section, and give this article a 🌟. Building Intelligent Feedback Systems: A Deep Dive into Conditional Agentic Workflows with… https://pub.towardsai.net/building-intelligent-feedback-systems-a-deep-dive-into-conditional-agentic-workflows-with-c40daa159d74 was originally published in Towards AI https://pub.towardsai.net on Medium, where people are continuing the conversation by highlighting and responding to this story.