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Module 6: Interaction Design Framework

🧱 Module Purpose

To define how users will interact with the AI agent across different modalities and contexts. This includes structuring conversations, designing error handling and escalation points, defining tone and personality, and supporting multimodal experiences. Grounded in the data boundaries and failure scenarios defined in Module 5, this module ensures agents are intuitive, trustworthy, and effective.

🔍 Sub-Components & Templates

1. Conversation Flow Design

Map typical user-agent dialogues:

  • Primary use cases and expected flows
  • Branching logic for user inputs
  • Handling ambiguous queries

Tools: flowcharts, decision trees, Miro, Lucidchart
Source: Gemini + CrewAI + LangGraph state planning

2. Tone, Personality & UX Principles

Define consistent user-facing voice:

  • Agent “personality” template (formal, casual, helpful, expert)
  • Language tone guidance (concise vs. verbose, emoji use, etc.)
  • Multilingual and accessibility considerations

Source: User onboarding psychology, GPT persona patterning

3. Human-in-the-Loop (HITL) Design

Define human intervention points:

  • Thresholds for escalation (confidence score, sensitive data)
  • Alerting workflows (Slack, email, dashboarding)
  • Approval check-ins for regulated flows

Source: Gemini HITL architecture recommendations

4. Escalation & Error Handling Logic

Design fallbacks for failed, incomplete, or uncertain interactions:

  • Graceful fallback responses
  • Triggered escalation to humans or alternative workflows
  • Logging for retraining and debugging

Tools: LangSmith, custom observability tooling

5. Multi-Modal Interface Planning

Plan user interactions beyond chat:

  • Web form + chatbot hybrid UX
  • Voice interface or visual annotation (if applicable)
  • File uploads (e.g. PDF, CSV) and data ingestion flows

Source: Gemini multi-modal design strategy

6. Onboarding & User Education

Structure first-use interactions:

  • Welcome message + purpose disclosure
  • Agent capabilities and limitations briefing
  • Tips, retry guidance, and escalation visibility

Key for adoption in both startup and enterprise contexts

7. Prompt Engineering Techniques

Standardize prompt styles and input/output constraints:

  • Chain-of-Thought and Tree-of-Thought patterns
  • Role instruction preambles
  • Function-calling and system prompt configuration

Source: Gemini prompt optimization methodology

📈 Success Metrics

  • Task Completion Rate (per use case)
  • User Satisfaction Score (via survey or thumbs-up)
  • Escalation Rate (must be low but safe)
  • First Interaction Success Rate
  • Retry/Error Frequency

🛠 Tool & Integration Suggestions

  • Flow Mapping: Miro, Whimsical, Lucidchart
  • Prompt Testing: LangSmith, Promptfoo, OpenAI Playground
  • Human-in-the-Loop Tools: Slack API, Zapier, internal alerting
  • Multimodal: Streamlit, Microsoft Bot Framework, Twilio for voice/SMS

📦 Reusable Templates Included

  • Conversation Flow Template
  • Persona & Tone Definition Sheet
  • HITL Escalation Matrix
  • Prompt Engineering Pattern Sheet
  • Onboarding Message Library
  • Multi-modal Planning Canvas

🔄 Development Tracks Mapping

Track Flow Outcome
Weekend Warrior Prebuilt tone + minimal flow + fallback Clean first-use UX with self-contained agent
Startup Conversational design + simple escalation + prompt testing Branded agent with defined tone, working prototype
Enterprise Multimodal support + HITL + fallback routing + compliance UI Trustworthy agent aligned with regulatory and user policies

🔗 External References to Incorporate

🔁 Dependency Links

  • Input: Data strategy, capability boundaries, escalation logic from Module 5
  • Feeds into: Module 7 (Rapid Development), Module 8 (Performance Evaluation)