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The Problem: LLMs Act Like General-Purpose Assistants

Traditional system prompts fail to keep agents focused on their specific role. Instead of maintaining their culture, they default to being helpful general-purpose assistants. The issue: Without Culture, agents with prompts easily:
  • Break character with casual language
  • Answer off-topic requests instead of staying focused
  • Act like generic AI assistants rather than their assigned role

The Solution: Culture Feature

Upsonic’s Culture keeps agents focused on their specific domain by extracting structured guidelines, wrapping them in XML tags for persistence, and ensuring agents stay on-topic for their role—not acting as general-purpose LLMs. Quick Start:

Side-by-Side Comparison

Here’s how Culture transforms agent behavior:

❌ Without Culture (Acts as General Assistant)

✅ With Culture (Stays Focused on Role)

Key Difference: Culture ensures agents stay focused on their specific domain rather than defaulting to general-purpose assistance.

Culture vs. User Memory

Configuration Options

Use repeat=True for:
  • Conversations with 10+ messages
  • Critical roles requiring strict consistency

Automatic Extraction

Culture analyzes your description and extracts structured guidelines:

Use Cases

Best Practices

Do:
  • Be specific: “You are a hotel receptionist” > “Be professional”
  • Define boundaries: Include topics to avoid and help with
  • Use repeat=True for 10+ message conversations
  • Test with casual language to verify persistence
Don’t:
  • Use vague descriptions like “Be helpful”
  • Set repeat_interval below 3 (becomes annoying)
  • Confuse with User Memory (Culture = agent persona, Memory = user traits)

Troubleshooting

Not maintaining character? Enable repeat=True and make description more specific. Extraction slow? Check network connectivity or use a faster model. View extracted guidelines:

Key Takeaways

  • Focused behavior: Culture keeps agents on-topic for their role instead of acting as general-purpose assistants
  • AI-powered extraction: Automatically extracts structured guidelines from natural descriptions
  • Persistent guidelines: Wrapped in XML tags and injected into system prompts for better adherence
  • Handles challenges: Maintains persona with casual language, off-topic requests, and direct challenges
  • Zero storage: No database required, adds ~100-300 tokens per interaction
  • Async extraction: First message may be slightly slower due to guideline preparation