Multi-Agent Design vs. Single Agents

Unlike solutions such as Adept AI’s ACT-1, which focus on a single powerful model acting alone, Hulcon employs a team-of-agents approach. Adept is building a large “action transformer” trained on web UIs and using a custom actuation layer, essentially a heavy one-model-does-all strategy.

Hulcon instead orchestrates multiple specialized agents (e.g. one for planning, others for specific tasks), coordinated by a Supervisor. This multi-agent architecture allows Hulcon to handle complex, varied tasks more flexibly than a solo agent. It also means Hulcon can incorporate the latest specialized models (vision, language, planning, domain-specific AI) as they emerge, rather than betting everything on a single proprietary model.

The result is a system that can leverage best-of-breed AI for each function and collaborate to solve problems, which is harder for single-agent systems to match.