Introduction

In the enterprise AI landscape, we are witnessing the rapid death of the "Linear RAG" pattern. For simple internal wikis, a basic retrieval-augmentation chain is sufficient. However, for a high-stakes customer service environment like Tigress Tech Labs, simple RAG wrappers fail. They suffer from Chatbot Myopia: a lack of control flow, no concept of stateful memory, and a total absence of programmatic safety boundaries.

When a customer moves from asking about a technical bug to requesting a billing refund in the same thread, a linear chain often hallucinates or leaks context from the previous topic. To reach production grade, we moved beyond the single-prompt paradigm and engineered Tigra AI—a multi-agent ecosystem built on LangGraph, Llama-3, and programmatic validation.

1. The Core Philosophy: From Chains to Cyclic Graphs

In production, support is rarely a straight line. It is a series of loops: clarifying questions, tool execution, and validation checks.

The Orchestration Layer
We utilized LangGraph to define our orchestration logic. Unlike LangChain’s traditional LLMChain, LangGraph allows us to define a StateGraph where cycles are first-class citizens. This is the difference between a scripted IVR and an autonomous agent.

In Tigra AI, the graph doesn't just "run"; it manages a shared state object that evolves as it moves through various nodes (Supervisor, RAG, Specialist, Guardrails).

# Definition of the Tigra AI Production Graph
workflow = StateGraph(AgentState)

# Define Nodes
workflow.add_node("input_node", input_node)
workflow.add_node("supervisor_node", supervisor_node)
workflow.add_node("secure_rag_node", secure_rag_node)
workflow.add_node("technical_support_node", technical_support_node)
workflow.add_node("billing_agent_node", billing_agent_node)
workflow.add_node("escalation_check_node", escalation_check_node)

# Define Routing
workflow.set_entry_point("input_node")
workflow.add_edge("input_node", "supervisor_node")
workflow.add_conditional_edges(
    "supervisor_node",
    route_to_specialized_agent,
    {
        "technical": "technical_support_node",
        "billing": "billing_agent_node",
        "general": "general_inquiry_node"
    }
)

2. State Management: The Backbone of Thread-Safety

A common failure in production AI is "context drift." If the state isn't managed strictly, the model might use technical context to answer a billing question.

We solve this using Pydantic-driven State Management. Our AgentState isn't just a dictionary; it’s a typed schema that tracks:

• Persistent Memory: Using LangGraph’s MemorySaver, we provide thread-safe checkpoints.
• Metadata Tracking: We track turn_count and sender identity to prevent the model from getting lost in deep conversations.

Architectural Pattern: Stateful Checkpointing By implementing a thread_id at the API level (FastAPI), Tigra AI can resume a conversation hours later with full context, without re-injecting the entire history into the prompt window, thus saving tokens and reducing latency.

3. Autonomous Routing: The Supervisor Node

Tigra AI utilizes a Supervisor-Worker pattern. The Supervisor node is an LLM-powered router that acts as the "brain" of the operation.

Its logic is purely classification-based. It identifies the user's intent—TECHNICAL, BILLING, or TOOL_REQUEST—and delegates the state to the corresponding specialist. This "Separation of Concerns" ensures that the Technical Agent is never burdened with billing logic, keeping its prompt focused and its performance high.

4. Local Inference Performance: Llama-3-8B-Instruct

For Tigress Tech Labs, data sovereignty and cost-predictability led us to local inference. We deployed Meta-Llama-3-8B-Instruct running on-premise.

Engineering for Low Latency: To make an 8B model production-ready on standard enterprise GPUs, we implemented the following:

• torch.float16: We use half-precision to fit the model into VRAM while maintaining 99% of the FP32 accuracy.
• device_map="auto": This allows the Transformers library to intelligently shard the model across multiple GPUs or CPU offloading if necessary.
  -Transformers Pipeline: We wrap the local model in a HuggingFacePipeline, allowing it to interface seamlessly with the LangChain/LangGraph ecosystem.

5. The Safety "Air-Gap": Guardrails AI

In a support environment, a single hallucinated policy can lead to legal liability. We implemented Guardrails AI as a programmatic validation layer—an "Air-Gap" between the LLM and the customer.

support_guard = Guard().use_many(
    ProfanityFree(on_fail="fix"),
    ToxicLanguage(threshold=0.5, on_fail=OnFailAction.EXCEPTION),
    PromptInjection(on_fail=OnFailAction.EXCEPTION),
    NoHallucinations(on_fail="refuse"),

    # Ensures the answer is relevant to the user query
    RelevanceToPrompt(on_fail="refuse"),

    RestrictedTopics(
        topics=["internal_passwords", "employee_home_addresses"],
        on_fail="refuse"
    ),
    CompetitorCheck(
        competitors=["CompetitorX", "CompetitorY"],
        on_fail="fix"
    )
)

Unlike "system prompting," which the LLM can ignore, Guardrails is a hard-coded check.

1. Profanity & Toxicity: Using ProfanityFree and ToxicLanguage filters.
2. Competitor Check: The CompetitorCheck rail ensures the agent doesn't inadvertently recommend or discuss rival services.
3. NoHallucinations: This is our most critical rail. It cross-references the LLM's output against the RAG context. If the LLM makes a claim not found in the services_policies.txt, the output is blocked and rewritten.
   Check out [Guardrails' doc ] (https://www.guardrailsai.com/docs)

6. The Knowledge Layer: ChromaDB & Document Loader

Our RAG system isn't a simple "load and forget." The document_loader.py implements a sophisticated chunking strategy:

• Recursive Character Splitting: We chunk documents by semantic boundaries (paragraphs/headers) rather than arbitrary character counts.
• Persistence: We use ChromaDB to persist vectors locally, ensuring that the system doesn't need to re-index 500MB of PDFs every time the container restarts.

7. Smart Escalation: The EscalationEvaluator

Even the best AI reaches its limit. The EscalationEvaluator is a heuristic engine that calculates a Confidence Score.

Handoff to a human agent is triggered if the score drops below 0.7. This score is derived from:

• Sentiment Analysis: Is the user's frustration rising?
• Complexity Check: Does the query contain phrases like "override," "make an exception," or "legal"?
• AI Uncertainty: Does the LLM response contain hedging language?

When triggered, the graph transitions to the ask_human node, and the metrics.py module increments the escalation_total counter in Prometheus.

8. Compliance & Observability: Prometheus Monitoring

You cannot manage what you do not measure. In production, we don't just log errors; we track AI performance.

Our monitoring/metrics.py tracks:

• escalation_total: The ratio of AI-resolved vs. Human-resolved tickets.
• guardrail_interventions_total: How many times the safety layer had to correct the model.
• Latency Tracking: The time delta between input_node and output_node.

The CTO Perspective: Monitoring "Guardrail Interventions" is our early warning system. If this number spikes, it indicates that our base model or prompts are drifting and require retraining or refinement.

Conclusion: Orchestration is the New Prompting

The leap from a "chatbot" to an "ecosystem" is defined by control. By using LangGraph for orchestration, Llama-3 for local intelligence, and Guardrails AI for safety, Tigra AI provides Tigress Tech Labs with a support system that is as reliable as a human team but scales like software.

The future of AI isn't about finding the "perfect prompt"—it's about building the perfect graph. In production, the architecture is the intelligence.

Github Repository URL:

https://github.com/AhmadTigress/Rag_System/tree/main

License

This project is licensed under the MIT License.

Connect with Me

• GitHub: AhmadTigress
• X (Twitter): @AhmadTigress
• Kaggle: davidrufaieneye