AutoGen by Microsoft provides a framework for building multi-agent AI systems where specialized agents collaborate on complex research tasks. Unlike single-prompt LLM calls, AutoGen orchestrates conversations between agents with different roles—researcher, critic, fact-checker,...
ZTABS builds multi-agent research systems with AutoGen — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. AutoGen by Microsoft provides a framework for building multi-agent AI systems where specialized agents collaborate on complex research tasks. Unlike single-prompt LLM calls, AutoGen orchestrates conversations between agents with different roles—researcher, critic, fact-checker, writer—that iteratively refine outputs through structured dialogue. Get a free consultation →
500+
Projects Delivered
4.9/5
Client Rating
10+
Years Experience
AutoGen is a proven choice for multi-agent research systems. Our team has delivered hundreds of multi-agent research systems projects with AutoGen, and the results speak for themselves.
AutoGen by Microsoft provides a framework for building multi-agent AI systems where specialized agents collaborate on complex research tasks. Unlike single-prompt LLM calls, AutoGen orchestrates conversations between agents with different roles—researcher, critic, fact-checker, writer—that iteratively refine outputs through structured dialogue. Its conversation patterns (two-agent chat, group chat, nested chat) model real research workflows where multiple perspectives improve quality. AutoGen supports human-in-the-loop interaction, letting researchers guide agent conversations and approve intermediate results at configurable checkpoints.
Each agent has a focused system prompt, tool access, and expertise area. A research agent searches papers, an analysis agent processes data, a critic agent finds flaws, and a writer agent produces reports. Specialization improves output quality.
Agents critique and improve each other's work through multi-turn conversations. A draft passes through review cycles—fact-checking, methodology critique, clarity editing—producing higher quality output than single-pass generation.
Configure agents to pause for human approval at key decision points. Researchers can redirect agent focus, correct factual errors, and approve intermediate findings before the system proceeds.
Agents can execute code, search the web, query databases, and call APIs. A research agent with arXiv access, a data agent with Python execution, and a visualization agent with plotting capabilities collaborate on complete analyses.
Building multi-agent research systems with AutoGen?
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Schedule a CallConfigure the Critic agent with a different LLM provider than the Research and Writer agents. Using Claude as the critic while GPT-4 generates research creates genuine diversity of perspective—different model training biases catch different types of errors and hallucinations.
AutoGen has become the go-to choice for multi-agent research systems because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| Agent Framework | AutoGen 0.4+ |
| LLM | GPT-4o / Claude 3.5 Sonnet |
| Search | Semantic Scholar API + arXiv |
| Code Execution | Docker-sandboxed Python |
| Storage | PostgreSQL + S3 |
| Frontend | Streamlit / Gradio |
An AutoGen research system defines a team of specialized agents: a Research Agent with access to Semantic Scholar and arXiv APIs for paper discovery, a Data Agent with sandboxed Python execution for statistical analysis, a Critic Agent prompted to find methodological flaws and missing references, and a Writer Agent that synthesizes findings into structured reports. The orchestrator uses AutoGen's GroupChat pattern to manage turn-taking—the Research Agent presents findings, the Critic challenges claims, the Data Agent runs verification analyses, and the Writer incorporates validated results. Each agent operates with its own LLM configuration (GPT-4o for research and writing, Claude for critique) optimized for its role.
Human checkpoints pause the conversation after literature review, methodology selection, and before final report generation for researcher approval. Tool execution runs in Docker containers for safety, with file outputs stored in S3. The system maintains a knowledge graph in PostgreSQL that accumulates findings across research sessions, enabling incremental research that builds on previous work.
Our senior AutoGen engineers have delivered 500+ projects. Get a free consultation with a technical architect.