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Enterprises today face a continuous stream of unstructured data, from emails and service requests to commercial documents and customer responses. Managing and routing this information manually is often resource intensive and impractical, leading to errors. This is where Agentic AI, especially when powered by Large Language Models (LLMs), revolutionizes enterprise workflows.
In this first blog of our Agentic AI series, we dive deep into how enterprises can leverage LLM-based text classification to intelligently route content toward the appropriate teams, departments, or autonomous agents. Building upon our foundational exploration (https://www.ivoyant.com/blogs/what-is-agentic-ai-whats-in-it-for-enterprises), we now focus on practical strategies that make LLMs effective enterprise classifiers.
Conventional routing mechanisms, such as keyword matching or static rule engines often fail to
These methods struggle especially in complex scenarios requiring understanding of context, intent, and nuance.
Models like GPT-4, Gemini, and Claude offer contextual intelligence and adaptive reasoning. But without structured workflows, their outputs can become verbose, inconsistent, or even irrelevant.
To fully realize their potential, enterprises must combine structured design, validation pipelines, and advanced orchestration frameworks.
1. Structured Prompting
Clearly instruct LLMs on expected outputs:
"Classify the following user intent into one of these categories: Planning, Activity Selection, Workflow Generation,Workflow Modification. Only respond with the category label."
This clarity helps ensure concise and relevant responses.
2. Few-Shot Examples
Provide the model with concrete examples:
Text: "I want to create a new workflow to handle customer onboarding."
Classification: Workflow Generation
Text: "Can you add a new step to the existing process?"
Classification: Workflow Modification
3. Output Constraints via Schema
Force structured responses through specific formatting requirements, for example:
Use strict formats, such as a predefined JSON schema:
{"intent": "ActivitySelection"}
To go beyond static formatting, advanced system scan bind tools to LLM outputs using techniques like llm_bind_tools, which allow each node in a routing graph to enforce schema-bound outputs. These tool bindings not only validate output structure but also trigger the appropriate agents based on the schema matches, ensuring predictable, consistent behavior and enabling seamless integration with downstream systems.
4. Response Validation with Pydantic
Implement robust post-processing to manage unexpected outputs:
Validate model outputs using Pydantic schemas for input and output validation to ensure robustness and predictable structures.
5. Error Handling and Fallback Mechanisms
Ensure resilient systems using a multi-layered approach:
· Identifying Missing or Erroneous Information:
Use output schema validation (e.g., via Pydantic) to detect incomplete or malformed LLM responses. You can also build logic to catch low-confidence classifications or unknown intents (e.g., "Unclassified").
· Requesting Clarification from the User:
When essential information is missing (like intent or key parameters), the system proactively formulates a follow-up prompt to ask the user for the missing details. This turns a static classification step into an interactive loop, improving both accuracy and user engagement.
In our Co-pilot (a Gen AI application that has been created for ivoyant’s in-house product PlatformNX) System, the incoming user query must be correctly classified to route it to the appropriate agent.
This intent detection is crucial for seamless agent routing in our architecture.
These are the following agents (Co-pilot) that we are using,
1. Planning Agent: Plans the tasks and defines high-level workflows based on user intent.
2. Activity Selection Agent: Helps users identify possible activities or services available for orchestration.
3. Workflow Generation Agent: Builds detailed JSON workflows from planning outputs.
4. Workflow Modification Agent: Alters or updates existing workflows based on new requirements.
Using LLM-based intent classification, each user input is routed automatically to the most appropriate agent, enhancing automation and reducing human decision bottlenecks.
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Pydantic is used to enforce strict validation of:
1. Incoming user intents.
2. Outgoing workflow structures.
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This ensures consistency between model prediction sand the requirements of downstream agents.
LangChain provides out-of-the-box LLMChain classes, allowing you to,
1. Define prompt templates.
2. Set input and output schemas.
3. Chain multiple LLM calls with structured intermediate outputs.
This eliminates the need for custom classification frameworks from scratch and ensures rapid deployment.
As the system grows, LangGraph becomes essential.
LangGraph allows,
· Graph-based orchestration of agents.
· Dynamic routing based on intent, context, or prior actions.
· Conditional flows where output of one agent determine the next action.
In our case, the intent classification node (viaLLM) leads into different agent nodes depending on predicted intent.
Thus, LangGraph provides scalable, dynamic, and adaptive orchestration beyond simple sequential chains.
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Integrating LLMs, LangChain, Pydantic, andLangGraph delivers:
1. Higher classification accuracy.
2. Automated and scalable routing.
3. Reduced human intervention and errors.
4. Personalized and dynamic enterprise workflows.
5. Future-ready architecture that adapts as enterprise needs evolve
LLMs are more than text generators - they are becoming core engines of intelligent decision routing in modern enterprises.
By combining structured classification strategies, Pydantic validation, LangChain orchestration, and LangGraph dynamic routing, organizations can transform traditional workflows into adaptive, agentic ecosystems, unlocking new levels of automation, efficiency, and scalability.
Stay tuned for our next blog, where we’ll explore Prompt-Based Routing to further enhance agent selection precision in enterprise workflows.
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