AI Agent for Automated Request Processing

Development of an AI Agent for Request Processing

Every day, dozens of similar requests come into a company: password reset, VPN setup, billing questions. Operators spend 70% of their time on classification and data collection. Manual processing takes an average of 4 hours — an AI agent handles it in seconds, a 1000x speed improvement. Our AI agent for requests delivers request processing automation, AI request classification, request routing, dialogical agent, RAG for support, Jira integration, incident processing, NLP for requests, request data collection, structured outputs, and fine-tuning for requests.

Problems Solved by the AI Agent

Chaotic classification is the first issue. Operators manually assign categories, often making mistakes (up to 15% misrouting). The agent uses Structured Outputs from OpenAI: the model returns strictly typed JSON, eliminating parsing errors. Temperature = 0 — predictability comes first.

The second problem is incomplete data. 22% of tickets miss key fields, leading to a chain of clarifications. The dialogical agent politely collects missing information, cutting the processing cycle by 60%.

How the AI Agent Classifies Requests

Classification is central. The agent parses the request, extracts category, subcategory, priority, and determines if a human is needed. The model leverages OpenAI's Structured Outputs feature: it returns a strictly typed JSON, eliminating parsing errors. As noted in OpenAI documentation, this scheme reduces errors to near zero. Temperature = 0 for consistency.

from pydantic import BaseModel
from typing import Optional, Literal
from openai import OpenAI
import json

client = OpenAI()

class RequestClassification(BaseModel):
    category: Literal["billing", "technical", "account", "shipping", "legal", "other"]
    subcategory: str
    priority: Literal["low", "normal", "high", "critical"]
    requires_human: bool
    missing_fields: list[str]
    confidence: float  # 0-1

def classify_request(request_text: str) -> RequestClassification:
    """Classify request via Structured Outputs"""
    response = client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "Classify the incoming request."},
            {"role": "user", "content": request_text},
        ],
        response_format=RequestClassification,
        temperature=0,
    )
    return response.choices[0].message.parsed

def collect_missing_info(request_text: str, missing_fields: list[str]) -> str:
    """Formulate a clarifying question to collect missing data"""
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{
            "role": "system",
            "content": "Formulate a polite question to clarify information for the request."
        }, {
            "role": "user",
            "content": f"Request: {request_text}\nMissing fields: {missing_fields}"
        }],
    )
    return response.choices[0].message.content

Dialogical Agent for Data Collection

When the model detects missing fields, the dialogical agent initiates a conversation. It politely inquires until all required attributes are collected. Each category has its own template: billing needs invoice number and amount, tech support needs product version and error description.

Example: User: "I can't log into CRM, error 500." Agent: "Please specify your email and the CRM name." User: "[email protected], Bitrix24." Agent: "Thank you. Ticket #12345 created. Engineers will respond within an hour."

class RequestProcessor:
    """Dialogical agent for complete request data collection"""

    TEMPLATES = {
        "billing": ["invoice_number", "amount", "payment_date"],
        "technical": ["product_name", "version", "error_description", "steps_to_reproduce"],
        "account": ["user_email", "account_id", "issue_description"],
    }

    def __init__(self):
        self.conversations: dict[str, list] = {}
        self.collected_data: dict[str, dict] = {}

    def process_message(self, session_id: str, message: str) -> str:
        if session_id not in self.conversations:
            self.conversations[session_id] = []
            self.collected_data[session_id] = {}

        self.conversations[session_id].append({"role": "user", "content": message})

        # Update collected data
        self._extract_and_update(session_id, message)

        # Check completeness
        required = self._get_required_fields(session_id)
        missing = [f for f in required if f not in self.collected_data[session_id]]

        if not missing:
            return self._finalize_request(session_id)

        # Request next field
        return self._ask_for_field(session_id, missing[0])

    def _ask_for_field(self, session_id: str, field: str) -> str:
        field_questions = {
            "invoice_number": "Please provide the invoice or bill number",
            "amount": "What is the amount on the invoice?",
            "error_description": "Please describe the error in more detail",
        }
        return field_questions.get(field, f"Please specify: {field}")

    def _finalize_request(self, session_id: str) -> str:
        data = self.collected_data[session_id]
        ticket_id = create_ticket(data)
        return f"Ticket created: #{ticket_id}. We will contact you within 24 hours."

Zero-shot vs Fine-tuning: How to Choose?

For most scenarios, a zero-shot prompt with instructions suffices — 85-90% accuracy. If categories are specific (e.g., legal or medical requests), fine-tuning on 500+ historical tickets boosts accuracy to 97%. Comparison below:

What the AI Agent Delivers: A Case from Our Practice

In a company with 800 employees, we deployed the agent for IT support. Categories: system access (34%), hardware (22%), software (18%), network (14%), other (12%). Before deployment, operators spent 70% of time on initial processing — after the agent, load dropped to 30%. Results:

• Auto-resolution L1 (response without engineer): 41%.
• First response time: from 4 hours to instant — that's 240x faster than manual.
• Categorization accuracy: 93% (25% higher than manual).
• Data completeness in tickets: rose from 78% to 96%, a 23% improvement.

Support budget savings: $30,000 per year — from reduced L1 load. The project cost typically ranges from $15,000 to $50,000 depending on complexity, but ROI is achieved within 3–4 months. Annual operational cost savings reach $50,000 with volumes over 10,000 tickets.

Comparison: Manual vs AI Agent

Common Implementation Mistakes

First: insufficient testing on rare scenarios. We recommend a dataset of 500+ real requests. Second: ignoring human oversight for critical requests. We always leave the operator the ability to intervene when confidence is below 0.8.

Errors and Guarantees: How We Achieve 95% Accuracy

Before launch, we prepare a dataset of historical tickets, tune prompts, and test on a sample of 500+ requests. After deployment, we monitor metrics: categorization accuracy (target >90%), escalation rate, information completeness. Monthly retraining on new data. We use NLP for sentiment analysis and auto-replies for frequent requests. We ensure a consistent SLA for response time and accuracy. Our experience: over 50 deployments in 5 years.

What Is Included in the Work (Deliverables)

1. Audit of the current request processing flow.
2. Design of classification and dialog logic.
3. Development of the agent (classification + data collection + RAG) with vector DB integration.
4. Integration with ticketing system (Jira, Zendesk, Bitrix24) via REST API.
5. Testing on historical data and A/B test on live requests.
6. Documentation, operator training, 1-month warranty support.

Timeline and Cost

• Classification + routing agent: 2–3 weeks.
• Ticketing system integration: 1–2 weeks.
• Knowledge base (RAG): 2–3 weeks.
• Testing and tuning: 1–2 weeks.
• Total: 6–10 weeks.

Cost varies and is calculated individually based on category complexity, data volume, and number of integrations. With over 5 years of experience and more than 50 successful deployments, we have fine-tuned our approach across industries. Request a demo or consultation for your scenario — we will conduct an audit and propose the optimal solution within 1–2 days.