Next-Generation AI Agents for Real-World Applications in the Apply AI sectors (RIA) (Partnership in AI, Data and Robotics)

Expected Outcome:While today’s AI agents still have limited capabilities, advances in model architecture, memory, reasoning, and autonomous behaviour are paving the way to unlocking their potential across economic sectors. The Apply AI Strategy[1]acknowledges this trend and the need to advance research on next-generation AI agents. Project results are expected to contribute to some of the following expected outcomes: Significant improvements in the autonomy, robustness and reliability of AI agents through advanced planning mechanisms, memory management, and reasoning capabilities.Innovative multi-agent frameworks and protocols demonstrating effective decentralized coordination and collaboration among multiple AI agents beyond the capabilities of individual agents. Scope:Next-generation AI agents are autonomous systems powered by large AI language models (e.g., large language models or large multimodal models), that can plan, utilize tools and perform actions autonomously to achieve specified goals based on high-level instructions. The large AI model acts as the agent's "brain," capable of interpreting instructions, generating plans, and using tools. This capability enables agents to autonomously plan and adapt behaviour in real-time to accomplish complex, multi-step tasks. AI Agents hold significant promise in numerous applications areas such as data analytics and coding.Effective AI agents require careful design, incorporating structured planning and reasoning methods to manage complex tasks, and be equipped with appropriate validation and monitoring techniques. Multi-agent collaboration frameworks further enhance capabilities by enabling structured interactions among multiple agents.Key aspects in designing effective AI agents include robust planning, reasoning, and search mechanisms that allow agents to approach complex tasks by breaking them down into structured subgoals. Effective memory and state management are necessary to maintaining coherent long-term inter