Trail Guide Agent Specification

Summary

The Trail Guide Agent is an AI-powered conversational assistant that helps outdoor adventure enthusiasts plan hiking trips by recommending trails and gear. The agent provides personalized recommendations based on user experience level, preferences, location, and weather conditions, while maintaining natural conversation flow across multiple interactions.

User Stories

As an adventure traveler, I want to ask natural language questions about hiking trails so that I can discover suitable outdoor experiences without searching multiple websites.

As a beginner hiker, I want recommendations that match my fitness level and experience so that I can safely enjoy outdoor activities.

As a family planner, I want age-appropriate trail suggestions for my teenagers so that everyone can participate safely.

As a gear shopper, I want personalized equipment recommendations based on my planned activities so that I purchase or rent the right items.

As a safety-conscious hiker, I want current weather and trail condition information so that I can make informed decisions about my trip.

Acceptance Criteria

  • Agent responds to natural language queries about trails and gear in conversational tone
  • Agent maintains conversation context across multiple turns (minimum 5 exchanges)
  • Agent provides trail recommendations filtered by difficulty level (beginner, intermediate, advanced)
  • Agent provides trail recommendations filtered by location (geographic area or proximity)
  • Agent recommends gear based on planned activities and weather conditions
  • Agent responses include specific, actionable information (trail names, gear items)
  • Agent handles out-of-scope questions gracefully by explaining limitations
  • Agent response time averages under 3 seconds for typical queries
  • Agent produces responses that are factually grounded (no hallucinations of non-existent trails or locations)

Functional Requirements

Conversation Management

  • Agent accepts text input from user (command line or chat interface)
  • Agent maintains conversation history for context awareness
  • Agent uses conversation history to provide relevant follow-up responses
  • Agent supports multi-turn conversations without losing context
  • Agent recognizes when user changes topic and adapts accordingly
  • Agent can clarify ambiguous requests by asking follow-up questions

Trail Recommendations

  • Agent provides trail recommendations based on:
    • User experience level (beginner, intermediate, advanced)
    • Geographic location or region
    • Difficulty rating
    • Distance and elevation gain
    • Estimated completion time
  • Agent explains why specific trails are recommended
  • Agent provides trail difficulty ratings with clear descriptions
  • Agent mentions proximity to Adventure Works locations when relevant

Gear Recommendations

  • Agent analyzes planned activities to recommend appropriate gear
  • Agent considers weather forecasts when suggesting equipment
  • Agent provides specific gear items from Adventure Works inventory
  • Agent suggests rental vs. purchase options when applicable
  • Agent creates packing checklists tailored to specific adventures

Information Grounding

  • Agent bases recommendations on actual trail data (when available in knowledge base)
  • Agent cites sources or indicates confidence level in recommendations
  • Agent does not fabricate trail names or locations
  • Agent acknowledges limitations when information is not available

Response Quality

  • Agent responses are conversational and friendly in tone
  • Agent responses are concise but informative (typically 3-5 sentences)
  • Agent provides structured information when listing multiple options
  • Agent avoids jargon unless explaining technical trail/gear terms
  • Agent personalizes responses based on user’s stated preferences

Non-Functional Requirements

Performance

  • Agent responds to user queries within 3 seconds average response time
  • Agent handles concurrent conversations (minimum 10 simultaneous users in development)
  • Agent maintains conversation context without performance degradation

Quality and Safety

  • Agent responses are factually accurate and grounded in knowledge sources
  • Agent avoids generating harmful, biased, or inappropriate content
  • Agent declines to provide medical or emergency safety advice beyond general trail safety
  • Agent does not provide financial advice or guarantee pricing
  • Agent responses are evaluated for quality, relevance, and groundedness

Integration

  • Agent connects to Azure OpenAI Service for language model capabilities
  • Agent uses latest Microsoft Foundry SDK (azure-ai-projects) for implementation
  • Agent authenticates using Azure CLI credentials (DefaultAzureCredential)
  • Agent retrieves configuration from environment variables (endpoint, deployment name)
  • Agent logs interactions for evaluation and monitoring purposes

Scalability

  • Agent design supports future integration with:
    • Vector search for retrieval-augmented generation (RAG)
    • Weather APIs for real-time conditions
    • Inventory databases for gear recommendations

Educational Context

  • Agent implementation demonstrates GenAIOps best practices
  • Agent code is clear, well-commented, and suitable for learning purposes
  • Agent setup requires minimal configuration (runs with Azure OpenAI access)
  • Agent can be run locally by individual learners with their own Azure subscription

Edge Cases

Out-of-Scope Queries

  • User asks about destinations outside available knowledge: Agent responds politely that information is not available and offers to help with covered regions
  • User asks medical questions: Agent declines to provide medical advice and suggests consulting healthcare professionals
  • User asks for booking confirmation: Agent explains it provides recommendations only, not booking capabilities (in this version)

Conversation Boundaries

  • User provides very vague input (“I want to go hiking”): Agent asks clarifying questions about location, experience level, and preferences
  • Conversation exceeds token limit: Agent summarizes previous discussion and continues with fresh context window
  • User switches topics abruptly: Agent acknowledges topic change and provides relevant response to new topic

Data Quality Issues

  • Agent has conflicting information: Agent acknowledges uncertainty and provides most reliable information available
  • Trail data is outdated: Agent provides available information with disclaimer about verifying current conditions
  • No matching trails for criteria: Agent suggests loosening criteria or alternative nearby options

Technical Failures

  • Azure OpenAI Service unavailable: Agent displays clear error message and suggests retrying
  • API rate limit exceeded: Agent implements retry logic with exponential backoff (development environment)
  • Invalid API key or credentials: Agent provides clear authentication error message at startup

User Experience

  • User provides extremely long input: Agent processes first 1000 characters and asks user to break request into smaller parts
  • User expects real-time weather: Agent explains current version uses general seasonal weather patterns, not real-time data
  • User expects inventory availability: Agent explains recommendations are examples, not live inventory checks

Out of Scope (Future Enhancements)

The following features are explicitly not included in the initial version:

  • Real-time booking capabilities
  • Payment processing
  • Live weather API integration
  • Vector search / RAG implementation (may be added in stretch modules)
  • Multi-language support
  • Voice interaction
  • Mobile app interface
  • User authentication and profile storage
  • Trip history tracking
  • Email notifications
  • Integration with external booking systems

Success Criteria

The Trail Guide Agent is considered successful when:

  1. Functional completeness: All acceptance criteria are met
  2. Response quality: Agent provides helpful, relevant, grounded responses in conversational evaluations
  3. Educational value: Code demonstrates GenAIOps principles clearly for learners
  4. Runnable: Individual learner can run agent locally with minimal setup (<15 minutes)
  5. Measurable: Agent outputs can be evaluated using quality evaluators (relevance, groundedness, coherence)

Technical Constraints (from Constitution)

This specification aligns with the project constitution:

  • Azure-only: All cloud resources hosted on Microsoft Azure
  • Python implementation: Agent code written in Python 3.11+
  • Latest SDK: Uses Microsoft Foundry SDK (azure-ai-projects)
  • Simple authentication: Uses DefaultAzureCredential for local development
  • No secrets in code: API keys and endpoints stored in environment variables or Azure Key Vault
  • Minimal approach: Implements only essential features for educational purposes
  • Fast setup: Designed for individual learners with personal Azure subscriptions