We've touched on the four built-in conversation shapes; now you need to make them survive contact with the real world.
In the first post — One Coherent Agent Isn't Enough — you opened a channel, watched agents take turns, and saw the hub fold envelopes into a durable thread. That's the shape. This post is about the dials on top of that shape — the knobs that turn a loose multi-agent free-for-all into something you'd actually run when an agent goes quiet at 2am, a step needs to time out, or a sub-conversation has to stay off the main thread.
A single agent is a great starting point, but real work extends beyond just one.
Real work spans people, teams, services, and machines. A support escalation touches a triage bot, a knowledge agent, an on-call engineer, and a postmortem writer. None of them is "in charge" — they each take a turn, in the open, over a shared thread that outlives any one of them.
That's what the AG2 Network is built for: a layer where stateful, identity-bound, choreographed actions live. By the end of this post you'll have run all four conversation shapes the network ships with — and have a flavor for AG2's new multi-agent network, which we'll expand on in upcoming posts.
OpenAI recently announced advanced voice intelligence in the API, including GPT Realtime 2 for lower-latency, more natural spoken interactions. AG2 Beta wraps that class of model behind LiveAgent: one bidirectional session, continuous audio in and out, and provider-side voice activity detection so users can speak and interrupt like on a phone call—not like a walkie-talkie app.
In this post we walk through why that matters, how LiveAgent compares to the classic STT → Agent → TTS stack, and how to add tools and subagent-style delegation without giving up the realtime voice surface.
The original AutoGen and later AG2 architecture helped define the early agent ecosystem. It enabled real systems, shaped how many developers thought about agent orchestration, and gave us firsthand experience building and operating agent applications in practice.
That experience also made the limits of the original design clearer over time. As the agent ecosystem matured, expectations changed. Agents increasingly needed to fit into real application environments with concurrent users, explicit session boundaries, platform identities, persistence layers, and integration points that could not be treated as incidental details.
We found that some of these needs were challenging to address cleanly inside the original framework model. In many cases, shipping more production-suitable behavior meant adding complexity around the edges instead of improving the core abstraction itself, taking the focus of developers away from building agentic capabilities. That is a big part of why we decided to create AG2 Beta: a new framework track built around lessons we learned from the original AG2 to better support modern, real-world agentic systems.
You can read more about the motivation behind AG2 Beta in the AG2 Beta overview.
