[Chat Agent] Response Consistency - No Learning from Historical Interactions

Q&A
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2

Hello Luyujun,

You’re correct that Quick chat agents don’t retain learnings from past interactions across sessions. As per my understanding here’s how the system currently works:

  • Each query processes fresh using configured knowledge sources and current conversation context
  • Memory is user-specific and session-based: Chat agents can remember your preferences, context, and past interactions to personalize responses within conversations, but this is about remembering your preferences (like response format, frequently accessed resources, role context)
  • No cross-session learning: Agents don’t learn from historical interaction patterns to improve responses over time
  • 30-day conversation retention: Conversations are stored for only 30 days, after which context is lost

Refer : here

The most effective strategies based on Quick’s capabilities could be :

1. Reference Documents (Most Effective for Consistency)

Reference documents provide the strongest consistency mechanism:

  • Exact preservation: Quick preserves precise wording, formatting, and instructions exactly as written
  • Permanent context: These remain active in the agent’s memory across all interactions
  • Ideal for:
    • Metric definition files with calculation formulas and business context
    • Standard response formats and templates
    • Complex workflows that must follow exact sequences
    • Domain-specific terminology and business rules

Supported formats: See here

2. Custom Agent Instructions

You may use persona instructions to define preferred analysis approaches and response styles

3. Spaces for Knowledge Organization

Upload standardized documentation to Spaces for searchable, consistent reference material

Your Questions

Q: Has anyone developed effective reference document templates? What types of documentation in Spaces have been most helpful for standardizing agent response ?

I think the reference documents would work best when they contain:

  • Exact process documentation with step-by-step workflows
  • Response templates with specific formatting
  • Lookup tables and reference data
  • Brand-specific tone and terminology guidelines

Refer: here

Q: Are there plans for agents to learn from interaction patterns?
It seems system is designed around configured knowledge sources rather than adaptive learning from usage patterns.

Q: How do you ensure consistency when multiple team members query the same agent?

  • Share custom agents with standardized reference documents
  • Use Spaces to centralize knowledge that all team members can access
  • Configure agent persona instructions that define consistent behavior
  • Document standard operating procedures in reference documents

For your reference documents, consider organizing them as:

  1. Metric Definitions Document: Formulas, business rules, calculation methods
  2. Response Format Templates: Standard table structures, visualization preferences
  3. Workflow Documentation: Step-by-step analysis procedures
  4. Terminology Guide: Domain-specific terms and their definitions
  5. Data Source Mapping: Which dashboards/datasets to use for specific queries

Hope this gives some insight.

Cheers,

Deep

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