Custom Multi-Agent AI Systems Development with Guarantee

Recent project: a client from the fintech sector wanted to automate due diligence within two weeks. A single LLM agent couldn't handle it — it hallucinated in legal sections and lost context when analyzing 200+ contracts. We split the task among specialized agents: financial, legal, HR, and risk. Result — due diligence in 3 days instead of 4 weeks, with quality confirmed by an independent audit. This is an example of how a multi-agent system (MAS) solves tasks unattainable by a single agent.

Multi-agent systems are distributed AI systems where agents coordinate to perform complex tasks. Each agent is narrowly specialized, reducing hallucination rates and improving accuracy. Agentic AI allows delegating tasks to specialized agents. We use MAS in projects with heterogeneous data, multi-step reasoning, and expert reviews. Our track record: 10+ MAS implementations in finance and retail. We guarantee each section's quality through a human-in-the-loop process. Our team is certified in OpenAI and LangChain.

Architectures of Multi-Agent Systems

The Supervisor pattern uses a central orchestrator that distributes tasks among agents. It's simple to manage, but the orchestrator becomes a bottleneck. Peer-to-peer allows agents to communicate directly — the system is fault-tolerant, but debugging is more complex. Hierarchical organizes multi-level governance, which scales well but is overkill for small tasks. Pipeline represents a linear chain — predictable, but no feedback.

How the Supervisor Pattern Works in Practice

from langgraph.graph import StateGraph, END from langchain_openai import ChatOpenAI from typing import TypedDict, Literal import operator from langchain_core.messages import HumanMessage class MultiAgentState(TypedDict): messages: list current_task: str task_result: str next_agent: str # Specialized agents def researcher_agent(state: MultiAgentState) -> MultiAgentState: """Agent for information retrieval""" llm = ChatOpenAI(model="gpt-4o") task = state["current_task"] # Retrieval + analysis docs = retriever.invoke(task) context = "\n".join([d.page_content for d in docs]) result = llm.invoke([ HumanMessage(content=f"Research task: {task}\n\nContext:\n{context}\n\nExtract key facts:") ]).content return {**state, "task_result": result, "next_agent": "writer"} def writer_agent(state: MultiAgentState) -> MultiAgentState: """Agent for text generation""" llm = ChatOpenAI(model="gpt-4o") research = state["task_result"] original_task = state["current_task"] result = llm.invoke([ HumanMessage(content=f"Write an answer to the task: {original_task}\n\nMaterials: {research}") ]).content return {**state, "task_result": result, "next_agent": "reviewer"} def reviewer_agent(state: MultiAgentState) -> MultiAgentState: """Agent for quality check""" llm = ChatOpenAI(model="gpt-4o") draft = state["task_result"] review = llm.invoke([ HumanMessage(content=f"""Check the following text for: 1. Factual errors 2. Completeness of answer 3. Structure and clarity Text: {draft} If everything is fine, answer "APPROVED". Otherwise, specify concrete corrections.""") ]).content if "APPROVED" in review: return {**state, "next_agent": "complete"} else: return {**state, "task_result": review, "next_agent": "writer"} def supervisor_agent(state: MultiAgentState) -> MultiAgentState: """Orchestrator: determines the first agent for the task""" return {**state, "next_agent": "researcher"} def route_agent(state: MultiAgentState) -> str: return state["next_agent"] # Build the graph graph = StateGraph(MultiAgentState) graph.add_node("supervisor", supervisor_agent) graph.add_node("researcher", researcher_agent) graph.add_node("writer", writer_agent) graph.add_node("reviewer", reviewer_agent) graph.set_entry_point("supervisor") graph.add_conditional_edges("supervisor", route_agent) graph.add_conditional_edges("researcher", route_agent) graph.add_conditional_edges("writer", route_agent) graph.add_conditional_edges("reviewer", lambda s: END if s["next_agent"] == "complete" else s["next_agent"]) mas = graph.compile() 

In this example, the supervisor decides where to route the task, the researcher finds information, the writer produces an answer, and the reviewer checks quality. If the reviewer rejects it, the task goes back to the writer for revision. According to LangGraph documentation, StateGraph allows modeling complex feedback loops.

Why CrewAI Is Convenient for Rapid Prototyping

from crewai import Agent, Task, Crew, Process # Define agents with roles analyst = Agent( role="Financial Analyst", goal="Analyze financial data and identify trends", backstory="Experienced financial analyst with 10 years in investment banking", tools=[search_tool, calculator_tool, db_query_tool], llm=ChatOpenAI(model="gpt-4o"), verbose=True, ) report_writer = Agent( role="Report Writer", goal="Create professional financial reports", backstory="Business communication specialist with finance background", tools=[document_writer_tool], llm=ChatOpenAI(model="gpt-4o"), ) fact_checker = Agent( role="Fact Checker", goal="Verify all figures and statements in the report", tools=[search_tool, calculator_tool], llm=ChatOpenAI(model="gpt-4o"), ) # Tasks analysis_task = Task( description="Analyze financial indicators of Company X for the last reporting quarter", expected_output="JSON with KPIs: revenue, EBITDA, net_profit, growth_rates", agent=analyst, ) report_task = Task( description="Create an investment memorandum based on the analysis", expected_output="PDF-ready investment memorandum text", agent=report_writer, context=[analysis_task], ) # Crew crew = Crew( agents=[analyst, report_writer, fact_checker], tasks=[analysis_task, report_task], process=Process.sequential, verbose=True, ) result = crew.kickoff(inputs={"company": "LLC Example", "period": "last quarter"}) 

CrewAI allows declarative role and task definition without graphs. It suits prototypes and simple MAS. The Supervisor with LangGraph reduces latency by 40% compared to a P2P network, while CrewAI speeds up prototyping by 3 times.

MAS Tool Comparison

Practical Case: Due Diligence in 3 Days

We built a system for a fintech company handling M&A deals. Agent composition: Financial Analyst (IFRS, RAS), Legal (contracts, courts), HR (turnover, key employees), Risk (consolidated risk), Report (final document). Infrastructure: LangGraph with RAG indices for each agent. Results: time reduced from 4 weeks to 3 days, aspect coverage increased from 78% to 94%, cost per DD reduced by 67% — savings of 2 to 5 million rubles. Human-in-the-loop: final validation of each section.

What's Included in Multi-Agent System Development

• Architectural diagram with agent roles, exchange protocols, failure points.
• Baseline agents (3–5 pieces) with LLM settings, tools, RAG indices.
• Orchestration system (LangGraph or custom Supervisor) with error handling and retries.
• Monitoring dashboard (latency p99, token usage, rerun rate) — part of agent MLOps.
• Test polygon with 10+ scenarios covering normal and edge cases.
• Documentation in README format plus a December presentation for stakeholders.
• Team training (2–3 webinars on agent customization).

Development Stages

Timelines and Cost

Estimated timelines: 7 to 12 weeks depending on complexity. The cost of turnkey MAS development is calculated individually after an audit of your task. Request a consultation — we will evaluate your project and propose an architecture.

Communication Between Agents

# Pattern: agents pass structured messages via shared state class AgentMessage: source_agent: str target_agent: str message_type: str # "request", "result", "error" content: dict priority: int # Message queue for asynchronous communication import asyncio from asyncio import Queue class AgentCommunicationBus: def __init__(self): self.queues: dict[str, Queue] = {} def register_agent(self, agent_id: str): self.queues[agent_id] = Queue() async def send(self, msg: AgentMessage): await self.queues[msg.target_agent].put(msg) async def receive(self, agent_id: str) -> AgentMessage: return await self.queues[agent_id].get() 

Contact us to discuss your project. Get a consultation — we'll help you choose the optimal architecture.