Background
In recent years, AI Agent technologies have advanced rapidly, evolving from single-LLM applications into agents capable of planning, tool use, and autonomous decision-making, and have been widely applied in domains such as information retrieval, software development, and workflow automation. However, as task complexity continues to grow, a single agent is increasingly unable to cover all the required capabilities and scenarios. This has driven the rise of Multi-Agent architectures and, in turn, the emergence of the Agent-to-Agent (A2A) communication standard, which enables agents to advertise capabilities, exchange tasks, and report status, thereby achieving cross-agent collaboration. In the field of robotics, modular architectures that use foundation models as high-level agents for robots have likewise emerged, in which the agent interprets task instructions, plans, and executes actions through the robot's control interface via Model Context Protocol or Tool Calling.
Research Objectives
Although Agent and A2A architectures and Model Context Protocol have gradually matured, their practical adoption in the field of robotics still faces two major challenges. (1) Task orchestration: when multiple agents must jointly complete a single complex task, there is no unified mechanism for task parsing, assignment, coordination, and monitoring, which makes cross-agent collaboration difficult to scale and limits the joint operation of heterogeneous robots. (2) Agent development: the development of a robot agent involves the design, testing, version management, and deployment of multiple elements, including Agents, Prompts, Models, and Tools, yet no systematic development workflow or infrastructure has been purpose-built for this need. As a result, developers must repeatedly build similar components from scratch, iteration efficiency is constrained, and it becomes difficult to meet the demands of long-term operation and rapid evolution in real-world deployments.
Methods
To address the two challenges above, this project adopts the following two technical approaches. (1) Coordinator Agent design: based on a Multi-Agent architecture, a central agent is built to handle task planning, parsing, assignment, coordination, and monitoring, and it interoperates with other agents in accordance with the Agent-to-Agent (A2A) standard to coordinate multiple heterogeneous robots in completing complex tasks. (2) AgentOps infrastructure: a development pipeline is established that provides version management, automated testing, evaluation, and continuous integration / continuous deployment (CI/CD) for Agents, Prompts, Models, and Tools, and it integrates MLOps-based model lifecycle management with Kubernetes-based cloud-native deployment, offering a standardized agent development toolchain that helps developers rapidly build and iterate on agents.
Innovation
This project integrates cloud-native and multi-agent technologies, and follows the Agent-to-Agent standard to develop a platform for orchestrating and managing heterogeneous robots. Centered on a Coordinator Agent, the system enables task parsing, assignment, and coordinated control. It further incorporates AgentOps, MLOps, and Kubernetes to achieve full lifecycle automation of models and systems, thereby enhancing the efficiency and scalability of heterogeneous robot collaboration.
Expected Outcomes
This research adopts multi-agent technology to enable proxying and robot coordination. It leverages an Agent-to-Agent architecture to manage and orchestrate multiple heterogeneous robots for collaborative task execution. In addition, it integrates cloud-native architecture, AgentOps, and MLOps to achieve full lifecycle automation of models and systems, including management and deployment. Overall, the system provides the following benefits:
Horizontal specialization: The Agent-to-Agent architecture offers a well-defined abstraction layer, allowing teams and developers to focus on the development of AI models, robot functionalities, and tools, thereby improving development efficiency and modularity.
Vertical integration: Via adopting Model Context Protocol, the platform supports multi-robot collaborative applications and system integration, enabling teams and application developers to concentrate on application logic and implementation, accelerating solution deployment.