HIVEE

Background

As individuals and organizations increasingly develop their own AI agents trained on private data and workflows, these systems remain largely siloed. Existing tools focus on single-agent interactions, limiting the potential for complex, multi-domain problem solving. At the same time, real-world projects inherently require collaboration across different roles and expertise. Hivee emerges from this gap, addressing the need for an infrastructure where multiple agents can interact without requiring full centralization of data or control.

Process

The platform is designed as a multi-agent orchestration layer, integrating agent frameworks, tool access systems, and shared communication protocols. Agents are connected through a workspace interface where tasks can be distributed, executed, and monitored in parallel. The system supports structured task flows, inter-agent messaging, and human-in-the-loop escalation when conflicts arise. Development involved prototyping agent coordination logic, defining interaction protocols, and building a scalable architecture that allows agents to collaborate while preserving autonomy and data boundaries.