a2a-multi-turn

To implement multi-turn conversations for AI agents, use A2A patterns for input-required state management and context preservation. This handles iterative data collection and progressive refinement across back-and-forth agentic interactions.

Context preservation in conversational AI maintains conversation history across multiple turns. This mechanism ensures seamless user experiences and stateful communication protocols within agentic systems during iterative interactions.

Handle human-in-the-loop decision making in agentic systems by deferring decisions to a human when necessary. This approach uses input-required state handling to pause execution and request further information from the user.

Yes, you can use A2A protocols for iterative data collection. They manage input-required states where the agent asks for further information progressively, such as collecting destination, dates, and preferences for a travel booking agent.

Yes, this approach supports progressive refinement of agent outputs. It allows agents to progressively refine results based on user feedback through stateful, back-and-forth communication protocols within agentic systems.