AI Agent Development for Business Process Automation

AI Agent Development for Business Process Automation

Imagine: an accountant spends 3.5 hours a day reconciling invoices, and an HR manager spends 2 hours on onboarding each new hire. These processes consume time, but they can be automated. We are a team of AI/ML engineers with 10 years of experience. We develop intelligent AI agents based on large language models (LLMs) that take over routine work. Unlike classic RPA, our agents understand unstructured content: PDFs, photos, emails. They adapt to data variations without reprogramming. We have 30+ automation projects under our belt — from invoice processing to employee onboarding.

The first thing we do on a project is analyze your business process. We identify bottlenecks, collect typical cases, and design the future agent's architecture.

Why Is an AI Agent More Effective Than RPA?

RPA works with rigid scripts: any deviation in the structure of an email or PDF breaks the process. An AI agent powered by LLM (GPT-4o, Claude 3.5) handles variability. It extracts data from arbitrary text, uses Large language model (LLM) for classification, and makes decisions based on rules. The table below shows key differences.

How We Build an AI Agent: Step-by-Step Plan

Our approach to agent development consists of four stages:

1. Analysis and design (1–2 weeks): study the business process, collect data, draw AS-IS and TO-BE diagrams.
2. Core development (2–3 weeks): implement a state graph in LangGraph, connect the LLM (GPT-4o or Claude 3.5) and a vector database (ChromaDB/pgvector).
3. System integration (1–2 weeks): configure adapters for 1C, Jira, Bitrix24, email. The table below shows typical integrations.
4. Testing and HITL (1–2 weeks): verify on real data, configure human decision points.

Typical Business Processes for an AI Agent

Handling incoming requests: the agent parses email/form and classifies the input. It validates data, routes to the appropriate executor, and creates a task in the tracker. Other processes — onboarding, invoice processing, reporting — follow the same principle.

Agent Architecture for Request Processing

from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from typing import TypedDict, Optional

class ApplicationState(TypedDict):
    raw_input: str              # Incoming request text
    applicant_name: str
    application_type: str       # request type
    extracted_data: dict        # extracted data
    validation_result: dict     # validation result
    routing_decision: str       # where to route
    task_id: Optional[str]      # created task ID
    notification_sent: bool

llm = ChatOpenAI(model="gpt-4o", temperature=0)

def classify_and_extract(state: ApplicationState) -> ApplicationState:
    """Classify request and extract data"""
    response = llm.invoke(f"""Analyze the incoming request and extract structured data.

Request:
{state['raw_input']}

Return JSON:
{{
  "application_type": "vacation|expense|equipment|access|other",
  "applicant_name": "...",
  "department": "...",
  "details": {{}},  // specific fields by type
  "urgency": "normal|urgent|critical",
  "missing_info": []  // what is missing
}}""")

    import json
    data = json.loads(response.content)
    return {
        **state,
        "application_type": data["application_type"],
        "applicant_name": data.get("applicant_name", ""),
        "extracted_data": data,
    }

def validate_application(state: ApplicationState) -> ApplicationState:
    """Check completeness and policy compliance"""
    app_type = state["application_type"]
    extracted = state["extracted_data"]

    validation = {"valid": True, "errors": [], "warnings": []}

    if app_type == "vacation":
        # Check vacation balance
        days = extracted["details"].get("days", 0)
        balance = hr_api.get_vacation_balance(state["applicant_name"])
        if days > balance:
            validation["valid"] = False
            validation["errors"].append(f"Insufficient vacation days: requested {days}, available {balance}")

    elif app_type == "expense":
        amount = extracted["details"].get("amount", 0)
        if amount > 50000:  # Self-approval limit
            validation["warnings"].append("Requires manager approval")

    return {**state, "validation_result": validation}

def route_application(state: ApplicationState) -> ApplicationState:
    """Determine processing route"""
    app_type = state["application_type"]
    validation = state["validation_result"]
    urgency = state["extracted_data"].get("urgency", "normal")

    if not validation["valid"]:
        routing = "reject_with_explanation"
    elif app_type == "vacation":
        routing = "hr_manager"
    elif app_type == "expense" and state["extracted_data"]["details"].get("amount", 0) > 50000:
        routing = "director_approval"
    elif app_type == "access":
        routing = "it_department"
    else:
        routing = "auto_approve"

    return {**state, "routing_decision": routing}

def execute_routing(state: ApplicationState) -> ApplicationState:
    """Execute routing"""
    routing = state["routing_decision"]

    if routing == "auto_approve":
        task_id = jira_api.create_task(
            title=f"Auto-approved: {state['application_type']} from {state['applicant_name']}",
            status="Done",
            assignee="system",
        )
    elif routing in ["hr_manager", "director_approval", "it_department"]:
        assignee_map = {
            "hr_manager": "[email protected]",
            "director_approval": "[email protected]",
            "it_department": "[email protected]",
        }
        task_id = jira_api.create_task(
            title=f"Request for {state['application_type']} from {state['applicant_name']}",
            assignee=assignee_map[routing],
            description=json.dumps(state["extracted_data"], ensure_ascii=False),
            priority="High" if state["extracted_data"].get("urgency") == "urgent" else "Normal",
        )
    else:
        task_id = None

    notification_service.send(
        to=state["applicant_name"],
        message=f"Your request has been accepted. Routing: {routing}. ID: {task_id}"
    )

    return {**state, "task_id": task_id, "notification_sent": True}

# Build the process graph
from langgraph.checkpoint.memory import MemorySaver

graph = StateGraph(ApplicationState)
graph.add_node("classify_and_extract", classify_and_extract)
graph.add_node("validate", validate_application)
graph.add_node("route", route_application)
graph.add_node("execute", execute_routing)

graph.set_entry_point("classify_and_extract")
graph.add_edge("classify_and_extract", "validate")
graph.add_edge("validate", "route")
graph.add_edge("route", "execute")
graph.add_edge("execute", END)

application_agent = graph.compile()

Practical Case: Incoming Invoice Processing

Task: 180+ invoices for payment per month. Before automation, the chief accountant spent 3.5 hours per day. From our client's practice: we implemented the agent in 6 weeks.

Agent pipeline:

1. Extract text from PDF (pdfplumber / LlamaParse)
2. LLM extracts: supplier, TIN, amount, VAT, date, number, contract
3. Cross-check with contract register (vector search)
4. Verify in 1C: contract balance, budget line item
5. If OK – create a payment order in 1C
6. If discrepancy – assign a task to the accountant with an explanation

Metrics after 3 months:

• Automatically processed without intervention: 73%
• Accuracy of details extraction: 96%
• Errors (incorrect contract link): 1.2%
• Time savings: 2.5 hours/day

How Does Integration with Corporate Systems Happen?

We connect the agent to your systems via REST API, ODBC, or file exchange. Typical integrations: 1C, Jira, Bitrix24, mail servers. For each system, we build an adapter that translates data into a format the agent understands. A vector database (ChromaDB, pgvector) stores reference documents for RAG retrieval.

Human-in-the-Loop: When the Agent Requests Confirmation

def requires_human_approval(state: ApplicationState) -> bool:
    """Determines if human intervention is needed"""
    return (
        not state["validation_result"]["valid"] or
        state["extracted_data"].get("amount", 0) > 100000 or
        state["application_type"] == "termination" or
        state["extracted_data"].get("urgency") == "critical"
    )

# In LangGraph: interrupt_before for HITL
agent = graph.compile(
    interrupt_before=["execute"],  # Interrupt before execution
    checkpointer=MemorySaver(),
)

What Is Included in the Work

• Business process analysis (AS-IS and TO-BE diagrams)
• Agent architecture design (LangGraph, LLM, vector DB)
• Development and integration with your systems
• Testing with human-in-the-loop
• Documentation (model card, instructions)
• Employee training (2 hours)
• 3-month warranty and support

Indicative Timelines

• Analysis and design: 1–2 weeks
• Development with integrations: 3–5 weeks
• Testing and HITL setup: 1–2 weeks
• Total: 5–9 weeks depending on complexity

Order AI agent implementation for your business — contact us and we will propose architecture and timelines. Get a consultation and cost estimate.