[Feature] Support a2a protocol #2766
Description
Problem Description
Current chatbox interfaces are designed for human-to-AI interaction, but they fail to support seamless, secure, and structured communication between multiple AI agents operating behind the scenes. As enterprise workflows increasingly rely on multi-agent systems — where one agent researches, another drafts, a third validates, and a fourth coordinates execution — the lack of a standardized protocol for agent-to-agent (A2A) communication creates critical bottlenecks. Without A2A, agents must rely on brittle, custom-built bridges or human intermediaries to pass context, leading to data loss, inconsistent state, delayed responses, and operational fragility. This makes automated workflows unreliable, difficult to scale, and frustratingly inefficient for users who expect intelligent systems to collaborate as effortlessly as human teams.
Proposed Solution
Integrate the open-source Agent-to-Agent (A2A) protocol — developed by Google and adopted by over 50 enterprises — directly into the chatbox architecture to enable native, secure, and structured communication between AI agents. This would allow the chatbox to function not just as a human interface, but as a coordination hub for autonomous agent teams. The system should support automatic agent discovery, standardized JSON-based message exchange over HTTP/gRPC, encrypted context sharing, and role-based task delegation — all governed by the A2A specification. When a user initiates a complex request (e.g., “Analyze Q3 sales, draft a report, and schedule a presentation”), the chatbox should internally spawn, route, and synchronize multiple specialized agents using A2A, while still presenting a unified, human-readable response.
Additional Context
The A2A protocol acts as a universal translator for AI agents, regardless of their underlying architecture (e.g., LLM-based, rule-driven, or hybrid). It defines a common set of methods for agent discovery, task assignment, status updates, and error handling — all using open standards like JSON and HTTP/gRPC. For example, a research agent can securely send structured data (e.g., “{metric: ‘revenue’, value: 1.2M, source: ‘CRM_v3’, confidence: 0.98}”) to a drafting agent without requiring custom APIs or manual data reformatting. This interoperability is already being used in corporate environments for automated compliance checks, supply chain coordination, and customer service orchestration. By embedding A2A into the chatbox, we transform it from a passive input/output terminal into an active orchestration layer — enabling true autonomous agent collaboration. This aligns with the emerging paradigm of “multi-agent AI ecosystems,” where the most powerful outcomes emerge not from single models, but from coordinated teams of specialized agents communicating via a shared, secure protocol.
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