A framework for building self-improving AI agent systems from natural language goals, with generated agent graphs, human-in-the-loop control, and real-time observability built into the runtime.
Hive explores a different way to build agent systems: instead of hardcoding workflows, the user defines an outcome and the framework generates agent graphs and connection logic dynamically. The system is designed to monitor execution, capture failures, and evolve the workflow over time.
Builds structures from goals instead of forcing manual orchestration first.
Real-time visibility into execution, node behavior, and system decisions.
Intervention and guardrail support instead of blind autonomous execution.
Captures failures and pushes the workflow toward adaptation and recovery.