Rasa Pro Unveils Agent-to-Agent Protocol

Rasa's A2A protocol positions a primary Rasa agent as an "intelligent orchestrator" that manages the conversational flow and logic. This central agent controls when to involve external, specialized "sub-agents," how to hand off tasks, and how to maintain user context across these different interactions, even if the sub-agents are legacy systems or built on different tech stacks. This orchestration model is designed to support stateful, multi-turn interactions where context is preserved. The A2A protocol, currently in beta, enables agents to discover each other's capabilities and securely exchange information to accomplish goals without needing direct access to each other's internal memory or tools. This allows for more complex and robust conversational AI systems. The move toward multi-agent systems is a broader trend aimed at overcoming the limitations of single, monolithic AI models. By breaking down complex workflows into manageable steps handled by specialized agents, organizations can improve efficiency, scale automation, and reuse proven components across different processes. This approach is central to the evolution from simple RAG to more advanced Agentic RAG systems that can reason and act. Competitors are also heavily investing in agent-based architectures. Glean's "Work AI" platform includes an environment for building and deploying AI agents to automate everyday tasks across the enterprise. Hebbia employs a "multi-agent" or "agent swarm" system specifically for deep research and analysis of large document sets in sectors like finance and law. Meanwhile, Cohere is building out its enterprise AI platform, "North," which combines large language models with agentic capabilities to power custom applications. They are also enhancing their embedding models specifically to improve the search and retrieval functions crucial for these AI agents. This industry-wide shift highlights the growing importance of sophisticated, multi-step reasoning in enterprise AI.