hive-patterns

Best practices for building goal-driven LLM agents involve using structured prompts, modular incremental development, and judge patterns to ensure robust context management and efficient construction.

To manage context spillover in agent development, apply tiered compaction and spillover patterns. These approaches handle large data efficiently by structuring context across multi-turn interactions.

The fan-out fan-in execution pattern is an agent design approach for parallel task routing. It enables workflow automation by distributing sub-tasks to multiple nodes and aggregating the results.

Conditional edges enable workflow automation by routing agent logic dynamically between nodes. They allow an agent to evaluate conditions and trigger specific execution paths based on the interaction context.

Common anti-patterns in agent development include unstructured prompt design and monolithic execution flows. Avoiding these by adopting modular, incremental development and structured context management ensures robust orchestration.

Yes, you can implement client-facing interactions and feedback loops in LLM agents. Using multi-turn interaction patterns allows agents to gather requirements, research topics, and present findings effectively.