AutoGen (by Microsoft) is a framework for building multi-agent conversational systems where AI agents have structured conversations to solve tasks. Unlike CrewAI (role-based) or LangGraph (graph-based), AutoGen models agent interactions as conversations — agents talk to each...
ZTABS builds conversational ai agents with AutoGen — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. AutoGen (by Microsoft) is a framework for building multi-agent conversational systems where AI agents have structured conversations to solve tasks. Unlike CrewAI (role-based) or LangGraph (graph-based), AutoGen models agent interactions as conversations — agents talk to each other, debate solutions, and reach consensus. Get a free consultation →
500+
Projects Delivered
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Client Rating
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Years Experience
AutoGen is a proven choice for conversational ai agents. Our team has delivered hundreds of conversational ai agents projects with AutoGen, and the results speak for themselves.
AutoGen (by Microsoft) is a framework for building multi-agent conversational systems where AI agents have structured conversations to solve tasks. Unlike CrewAI (role-based) or LangGraph (graph-based), AutoGen models agent interactions as conversations — agents talk to each other, debate solutions, and reach consensus. This makes it natural for applications like code review (two agents discuss code quality), research (agents debate findings), and problem-solving (agents propose and critique solutions). AutoGen supports group chats with dynamic speaker selection, human-in-the-loop, and code execution.
Agents solve problems through structured dialogue — proposing solutions, requesting feedback, and iterating until quality thresholds are met.
Code executor agents write and run Python code in sandboxed environments. Results feed back into the conversation for analysis.
Humans can join agent conversations at any point — providing input, approving decisions, or redirecting the discussion.
Multiple agents in a group chat with dynamic speaker selection. The framework manages turn-taking, topic tracking, and conversation flow.
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Set strict termination conditions to prevent infinite agent conversations. Define maximum turns, quality criteria, and escalation triggers before deploying to production.
AutoGen has become the go-to choice for conversational ai agents because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| Framework | AutoGen 0.4+ |
| LLM | OpenAI / Azure OpenAI / Local |
| Code Execution | Docker sandbox |
| Backend | Python |
| Frontend | AutoGen Studio |
| Deployment | Docker / Azure |
An AutoGen conversational agent system defines assistant agents with specialized knowledge and a user proxy agent that represents human intent. For a code review application: the Developer Agent proposes code, the Reviewer Agent critiques it, and the Architect Agent checks design patterns — they have a structured conversation until the code meets all criteria. For data analysis: an Analyst Agent proposes queries, a Code Agent executes them, and a Reporter Agent summarizes findings.
Group chats enable all agents to participate, with the orchestrator selecting the next speaker based on conversation context. AutoGen Studio provides a visual interface for designing, testing, and monitoring agent conversations without writing code.
Our senior AutoGen engineers have delivered 500+ projects. Get a free consultation with a technical architect.