### Answer
This behavior—where the agent stays on a node, engages in extended conversation, and only later transitions—typically points to one or more of these issues:
1. Node instructions are too broad or complex
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If a single node contains instructions covering multiple topics or tasks, the LLM may "wander" within that node instead of transitioning. The debug guide recommends splitting nodes into smaller, focused units to improve consistency.
2. Transition conditions are not precise enough
If your transition conditions don't precisely match the intended triggers, the agent may not recognize when to move on. Review your conditions and consider breaking complex conditions into simpler ones.
3. Missing transition paths or edge cases
If the actual conversation deviates from the defined flow, the agent may stay in a node longer than expected. Adding global nodes for handling unexpected situations or making transition conditions more flexible can help.
Recommended steps:
- Split the problematic node into multiple smaller nodes, each handling a single task or topic.
- Review and refine your transition conditions to be more specific.
- Add finetune examples for transitions if the model struggles to understand your expectations.
- Consider adding global nodes to catch edge cases and allow users to skip or jump between nodes.
If you're still seeing issues after these changes, try adjusting the LLM temperature or switching to a more capable model for that node.