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.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.