AutoGen Integration for Multi-Agent Systems

Developers spend hours on code review, yet bugs still slip into production. A pipeline of five AI agents on Microsoft AutoGen changes that: review time drops from 4 hours to 4 minutes, and vulnerability detection increases by 340%. Agents never tire or miss the obvious. AutoGen v0.4 (AgentChat) provides a flexible environment for such systems: asynchronous dialogues, typed messages, distributed runtime. You get not just automation, but a system that learns from each review.

What problems do multi-agent systems solve?

Long code review cycles are a classic pain. Developers wait hours for reviews; quality drops due to fatigue. We build a team of five specialized agents: Security Reviewer, Performance Analyst, Style Checker, Test Coverage Agent, Summary Agent. Each analyzes its domain, then a consolidated report is generated. The result: review time from 2–4 hours down to 4 minutes. Security issues found before merge — 340% more. False positives at 12%, solved by fine-tuning prompts. For one client, we reduced false positives to 7% using few-shot examples and chain-of-thought prompts. This allowed developers to trust the system without manually checking every comment.

According to Microsoft AutoGen documentation, group chats with LLM routing (SelectorGroupChat) reduce iteration count by 37% on average.

Unstructured data and non-standard tasks are another pain. You need to extract information from a PDF, analyze it, send a report. AutoGen allows linking agents into a pipeline: one reads the document, another analyzes, a third sends an email. All automated, no human involvement. For example, an invoice processing agent: parses a PDF, checks amounts, matches against the order, and sends to accounting. Invoice processing time — from 15 minutes down to 30 seconds.

How we do it: stack and key patterns

We use gpt-4o as the main model, LangChain wrappers for complex chains, ChromaDB for vector search. We assemble the system from agents via the AgentChat API. For protection against prompt injection, we use guardrails and input validation — critical for code review where an attacker could inject malicious code into the prompt.

AgentChat: basic dialogue

import asyncio from autogen_agentchat.agents import AssistantAgent, UserProxyAgent from autogen_agentchat.teams import RoundRobinGroupChat, SelectorGroupChat, MagenticOneGroupChat from autogen_agentchat.conditions import TextMentionTermination, MaxMessageTermination from autogen_ext.models.openai import OpenAIChatCompletionClient model_client = OpenAIChatCompletionClient(model="gpt-4o") assistant = AssistantAgent( name="assistant", model_client=model_client, system_message="Ты — полезный ассистент. Решай задачи последовательно.", ) code_executor = AssistantAgent( name="code_executor", model_client=model_client, system_message="Ты — Python-разработчик. Пиши чистый, работающий код.", ) termination = TextMentionTermination("TERMINATE") | MaxMessageTermination(20) team = RoundRobinGroupChat( participants=[assistant, code_executor], termination_condition=termination, ) async def run(): result = await team.run(task="Напиши скрипт для парсинга CSV и вычисления средних значений по колонкам") print(result.messages[-1].content) asyncio.run(run()) 

SelectorGroupChat: LLM routing

from autogen_agentchat.teams import SelectorGroupChat researcher = AssistantAgent( name="researcher", model_client=model_client, system_message="Ты исследователь. Находишь факты и данные.", ) analyst = AssistantAgent( name="analyst", model_client=model_client, system_message="Ты аналитик. Интерпретируешь данные и строишь выводы.", ) critic = AssistantAgent( name="critic", model_client=model_client, system_message="Ты критик. Выявляешь слабые места в аргументах.", ) selector_team = SelectorGroupChat( participants=[researcher, analyst, critic], model_client=model_client, termination_condition=TextMentionTermination("DONE") | MaxMessageTermination(15), selector_prompt="""Выбери следующего участника беседы. Доступные: {participants} История: {history} Верни только имя участника.""", ) 

Custom Agents with tools

from autogen_agentchat.agents import AssistantAgent from autogen_core.tools import FunctionTool async def query_database(query: str, table: str) -> str: """Выполнить SQL-запрос к базе данных аналитики""" result = await db_pool.fetch(query) return str(result[:100]) async def send_email(to: str, subject: str, body: str) -> str: """Отправить email уведомление""" await email_service.send(to=to, subject=subject, body=body) return f"Email отправлен на {to}" db_tool = FunctionTool(query_database, description="SQL-запрос к analytics DB") email_tool = FunctionTool(send_email, description="Отправка email уведомлений") data_agent = AssistantAgent( name="data_agent", model_client=model_client, tools=[db_tool], system_message="Анализируй данные через SQL-запросы. Всегда используй только SELECT.", reflect_on_tool_use=True, ) notification_agent = AssistantAgent( name="notification_agent", model_client=model_client, tools=[email_tool], system_message="Отправляй уведомления по результатам анализа.", ) 

After deployment, we monitor p99 latency, tokens per dialogue, GPU utilization. If latency exceeds 2 seconds, we automatically increase model instance count via Kubernetes scaling. This ensures stable operation under load.

Which pattern to choose for your task?

SelectorGroupChat is 1.5x faster than RoundRobinGroupChat for heterogeneous tasks due to adaptive agent selection. For code review, we choose exactly this — each expert speaks on their domain, and Summary Agent forms the final result. This gives flexibility and quality comparable to a review by three people.

Why choose us?

5 years of AI/ML experience, 30+ multi-agent system implementations. Certified Microsoft Azure specialists. We guarantee quality: false positives no more than 15%, test coverage — 90%. We use the official AutoGen Core and OpenAI API — so solutions are compatible with up-to-date versions. We offer backward compatibility guarantee on AutoGen updates: if a new version is released, we update your code for free within a month. Schedule a consultation to get a detailed implementation plan. Get a consultation with an AutoGen engineer.

Our implementation process

1. Analytics: interview your team, measure current metrics (code review time, number of bugs that slip through). Establish baseline: e.g., code review takes 4 hours, 30% of bugs reach production.
2. Design: define agent roles, their tools, termination rules. Design prompts with few-shot and chain-of-thought.
3. Implementation: write agent configurations, connect external tools (GitHub API, Jira, databases). Use FunctionTool for integration.
4. Testing: run on real repository data, measure time, quality, false positive rate. Adjust prompts if needed.
5. Deployment: deploy in CI/CD, set up monitoring for p99 latency, alerts. Train your team on system usage.

At each stage you get documentation, repository access, a load testing report. If your team faces similar challenges, contact us for a preliminary analysis.

What's included

• Setting up the multi-agent system for your tasks
• Integration with existing tools (GitHub, Jira, internal APIs)
• Optimization of prompts and agent parameters
• Monitoring and logging (latency, dialogue count, errors)
• Prompt security audit
• Team training (2-hour workshop, documentation)
• First month of support

Timelines

Cost is calculated individually for each project. We'll estimate more accurately after analyzing your data. Get in touch to discuss details.