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AI-Driven Chatbots: A New Era with Nested Chat and AutoGen
AI-driven chatbots have evolved significantly, transitioning from basic scripted replies to sophisticated, task-solving conversational agents. A pivotal innovation in this evolution is the concept of Agentic AI, where bots become collaborators rather than mere tools. Among the most advanced capabilities in this space is nested chat, which elevates chatbot communication to a new level.
In this blog post, we will explore how to build multi-agent nested chats using AutoGen, a powerful framework for designing agent-based conversational systems. You’ll discover what nested chat is, its importance, and the four essential steps involved in creating a responsive, intelligent, and context-aware chatbot that feels more like a team than a tool.
What Is Nested Chat?
Imagine a scenario where an AI agent is tasked with writing an article. During the process, it requires feedback. Instead of stopping everything, it seamlessly initiates a side conversation with a reviewer agent, obtains feedback, adjusts the content, and returns to the main conversation. This process exemplifies nested chat.
Unlike traditional, sequential agent interactions—where one agent speaks after another in a fixed order—nested chat allows agents to pause the main thread, engage in sub-conversations, and return with enriched, context-aware responses. It’s akin to having mini-meetings within a larger discussion, enabling depth, flexibility, and multitasking, all crucial for building sophisticated AI systems.
Why Does Nested Chat Matter?
Nested chats represent a significant shift in chatbot intelligence for several key reasons:
- Dynamic Conversations: Real-world conversations aren’t linear. Nested chats enable AI agents to mimic how humans digress, clarify, and naturally return to topics.
- Multitasking Abilities: Agents can manage multiple goals simultaneously—writing, reviewing, searching—without losing context.
- Increased Accuracy: Feedback loops within nested chats enhance the quality of final outputs, whether it’s a document, recommendation, or decision.
- Smarter Collaboration: Agents work as a team, not just individually, leveraging each other’s strengths in real-time.
These benefits are especially valuable in domains like content creation, research, technical support, and more.
The Power of AutoGen
AutoGen is a robust framework that allows developers to build and manage multi-agent conversations effortlessly. What makes AutoGen particularly unique is its support for conversation programming, where agents interact using natural dialogue flows instead of just scripted logic.
AutoGen’s architecture facilitates:
- Conversational autonomy
- Tool integration (like web search)
- Group chat management
- Nested chat orchestration
This framework enables the design of bots that feel more like specialized team members rather than generic assistants.
Use Case: Article Writing System
To illustrate how nested chat works in AutoGen, let’s consider a content generation workflow. The task is to write an article about Microsoft’s newly released Magentic-One agentic system.
Here’s how the system operates using nested agents:
- An Outline Agent creates a detailed structure for the article.
- A Writer Agent uses this outline to compose the article.
- A Reviewer Agent engages in multiple feedback rounds with the writer to refine the content.
This interaction between the Writer and Reviewer occurs within a nested chat, embedded in the larger article production process. Once the writing and reviewing are complete, the refined content is returned to the user.
The 4 Key Steps to Build Multi-Agent Nested Chats
Here’s how you can structure a nested chat system using AutoGen’s agent-based approach:
Step 1: Define the Outline Agent and Tool Access
Every great article starts with a solid outline. The first agent in our system is responsible for understanding the topic and generating a logical, well-organized structure for the article. To do this effectively, it may need access to external information sources.
With AutoGen, agents can connect to tools like web search APIs, enabling real-time information gathering. The Outline Agent uses these tools to enhance its knowledge before delivering the structure to the next agent, signaling task completion and allowing the workflow to progress.
Step 2: Design Writer and Reviewer Agents
Once the outline is ready, the Writer Agent takes over, tasked with fleshing out the sections while maintaining clarity, creativity, and alignment with the provided structure. However, it doesn’t work in isolation.
The Reviewer Agent—a critical partner—provides feedback on the article. This agent checks for coherence, tone, grammar, and overall quality. Instead of waiting until the end, the reviewer steps in midway, reviewing drafts and requesting improvements. This writing and reviewing loop is where nested chat comes into play.
Step 3: Register the Nested Chat Loop
Nested chat doesn’t occur automatically—it needs to be defined. In this step, we set the rules of engagement between the writer and reviewer.
Here’s what happens:
- When the writer begins drafting, a trigger condition activates a nested chat session.
- The reviewer initiates the first round of feedback.
- The writer responds with revisions.
- This process repeats for a set number of turns (e.g., two rounds).
- Once the reviewer is satisfied, the writer finalizes the article and exits the nested chat.
This model ensures the writer doesn’t deliver a rough first draft but a refined version backed by internal review, mimicking professional editorial workflows.
Step 4: Initiate the Main Conversation
With all agents and nested interactions defined, the system is ready for action. The User Proxy Agent, representing the end user, initiates the conversation by providing the topic (in our example, the Magentic-One system). This agent coordinates the entire interaction, ensuring outputs from each stage are correctly passed along.
Once the outline is generated, it’s sent to the writer. Then, the nested chat between writer and reviewer takes place, resulting in a polished, high-quality article. Finally, the article is returned to the user, ready to be published, shared, or reviewed further.
Conclusion
Nested chat is more than a technical upgrade; it’s a paradigm shift in how AI agents communicate, collaborate, and deliver value. With AutoGen, building these intelligent, layered interactions becomes intuitive, empowering developers to create AI systems that mirror real-world teamwork. As conversations become more dynamic and tasks more complex, nested chats provide the structure and flexibility needed to manage it all seamlessly. Whether for content creation, customer service, or beyond, this approach transforms chatbots into true collaborators.
