hive-patterns

To build robust AI agents with feedback loops, you implement conditional edge routing alongside judge patterns to gate acceptance decisions. This approach supports complex, goal-driven workflows by ensuring validation steps loop back dynamically until criteria are met.

To manage context windows for AI agents with large data, use tiered compaction patterns. This technique progressively summarizes or offloads context, preventing token overflow while maintaining the necessary background information for goal-driven tasks.

The best way to handle multi-turn conversations in production-grade agent workflows is using event loop nodes. This pattern facilitates continuous, client-facing interaction by cycling through user inputs and agent responses dynamically within the execution graph.

Yes, the framework supports parallel execution through a fan-out and fan-in pattern. This allows the agent to dispatch multiple concurrent tasks and aggregate the results, optimizing workflows that generate several distinct outputs simultaneously.

Yes, you can use judge patterns for validation, as the judge acts as the sole mechanism for acceptance decisions. This ensures that the agent only progresses or completes a task when explicit validation criteria are satisfied.

The core mechanism for routing in complex AI agent workflows is edge-based routing. By applying conditional edges, developers can direct the execution flow dynamically based on real-time state, enabling branching logic and robust feedback loops.