The current generation of AI agents has made significant progress in the automation of backend tasks such as summary, data migration and planning. Although effective, these agents generally work behind the scenes – triggered by predefined workflows and returning the results without involvement of users. However, as AI applications become more interactive, a clear need has emerged for agents who can collaborate directly with real -time users.
AG-IUI (agent-user interaction protocol) is an open protocol and focused on events designed to meet this need. It establishes a structured communication layer between the agents of IA Backend and the applications of Fronend, allowing real -time interaction through a structured JSON event flow. By formalizing this exchange, AG-Uis Facilitates the development of AI systems which are not only autonomous but also aware of the user and reactive.
From MCP to A2A to AG-IU: the evolution of agent protocols
The trip to AG-Uis was iterative. Came for the first time MCP (Message Control Protocol)allowing structured communication between modular components. SO A2A (agent-agent) The protocols allowed orchestration between specialized AI agents.
AG-Uu completes the image: it is the first protocol that explicitly folds the AI agents with frontal user interfaces. This is the missing layer for developers trying to transform LLM Backend workflows into dynamic, interactive and man -centered applications.
Why do we need AG-II?
So far, most AI agents were Backend – effective but invisible workers. Tools like Langchain, Langgraph, Crewai and Mastra are increasingly used to orchestrate complex workflows, but the interaction layer has remained fragmented and ad hoc. Personalized websocket formats, JSON hacks or fast engineering tips such as “Thought: \ nation:” were the norm.
However, when it comes to building interactive agents such as Cursor—What works side by side with users in coding environments – Casseless complexity. Developers face several difficult problems:
- UI streaming: LLMS produces the output gradually, so users must see token answers by token.
- Tool orchestration: Agents must interact with APIs, execute code and sometimes take a break for human feedback – without blocking or lose a context.
- Shared mutable condition: For things like code bases or data tables, you cannot return complete objects each time; You need structured diffs.
- Competition and control: Users can send several requests or cancel actions halfway. Threads and states of racing must be managed properly.
- Safety and conformity: The solutions ready for the company require the management of the COR, the Auth headers, the audit newspapers and the proper separation of the responsibilities of the customers and the server.
- Heterogeneity of the framework: Each agent tool – Langgraph, Crewai, Mastra – uses its own interfaces, which slows down front development.
What AG-IF brings to the table
AG-Uis offers a unified solution. It is a light event diffusion protocol that uses standard HTTP (with server or SSE events) to connect an agent backend to any front. You send a single message to your agent, then listen to a flow of structured events in real time.
Each event has:
- A type: for example text_message_content, tool_call_start, state_delta
- A minimum and typed payload
The protocol supports:
- Live token streaming
- Progress of the use of tools
- State the difficulties and fixes
- Error and life cycle events
- Multi-agent transfer
Developer experience: Plug-And-Play for AI agents
AG-Uis Delivered with SDKs in Typecript and Python, and is designed to integrate into almost all Backend – Openai, Olllama, Langgraph or Personalities agents. You can start in a few minutes using their Quick start guide and playground.
With AG-II:
- The components of the Front and the Backend become interchangeable
- You can deposit a React user interface using Copilotkit components with a hint modification
- Exchange GPT-4 for a local lama without changing the user interface
- Mix and match agent tools (Langgraph, Crewai, Mastra) via the same protocol
AG-Uis is also designed with performance in mind: use ordinary JSON via HTTP for compatibility, or go to a binary serializer for higher speed if necessary.
What AG-IU allows
AG-Uis It is not only a developer tool – it is a catalyst for a richer AI user experience. By standardizing the interface between agents and applications, It allows developers of:
- Build more quickly with fewer personalized adapters
- Deliver ux smoother and more interactive
- Debug and replay the behavior of agents with coherent newspapers
- Avoid locking sellers by freely exchanging the components
For example, a collaborative agent propelled by Langgraph can now share his plan live in a react user interface. An assistant based in Mastra can take a break to request confirmation from a user before executing code. AG2 and A2A agents can transparently change contexts while keeping the user in the loop.
Conclusion
AG-Uis is a major step for AI in real time and oriented by users. While LLM -based agents continue to grow in complexity and capacity, the need for a clean, extensible and open communication protocol becomes more urgent. AG -Sui offers exactly that – a modern standard for the construction of agents that are not simply actbut interact.
Whether you build autonomous co-pilots or light assistants, AG-II provides a structure, speed and flexibility to the Frontland-Agent interface.
Discover the GitHub page here. All the merit of this research goes to researchers in this project.
Thank you to the Tawkit team for leadership / opinion resources for this article. The Tawkit team supported us in this content / article.
Asif Razzaq is the CEO of Marktechpost Media Inc .. as a visionary entrepreneur and engineer, AIF undertakes to exploit the potential of artificial intelligence for social good. His most recent company is the launch of an artificial intelligence media platform, Marktechpost, which stands out from its in-depth coverage of automatic learning and in-depth learning news which are both technically solid and easily understandable by a large audience. The platform has more than 2 million monthly views, illustrating its popularity with the public.
