Chatbot-Based Architecture

Overview

Chatbot-based systems are designed for real-time, conversational interactions. They prioritize low latency, streaming responses, and immediate user feedback. This architecture is perfect for scenarios where users expect immediate responses and interactive tool calling.

Core Components

Channel/UI Layer

• Web/app chat interfaces with real-time messaging
• Voice interfaces for hands-free interaction
• Email bridge for asynchronous communication
• Multi-modal support (text, voice, images, files)

Gateway Layer

• Authentication and session management
• Rate limiting and abuse prevention
• Feature flags for gradual rollouts
• Load balancing and traffic routing

Orchestrator

• Turn loop management for conversation flow
• Pre/post guards for input/output validation
• Tool routing and execution coordination
• Context management across conversation turns

RAG Service

• Real-time retrieval APIs with low latency
• Re-ranking for relevance optimization
• Session-based filtering for personalized results
• Caching strategies for performance

Tool Registry

• Typed adapters for external services
• Search tools (web, internal knowledge)
• Calendar integration for scheduling
• CRM tools for customer data
• Custom business logic tools

Memory Store

• Per-user conversation memory
• Session persistence across turns
• User profile preferences
• Context summarization for long conversations

Eval Service

• Online quality checks in real-time
• Regression monitoring for model changes
• A/B testing for prompt optimization
• Feedback collection and analysis

Telemetry

• Distributed tracing across components
• Performance metrics and SLAs
• Cost tracking per conversation
• Audit logs for compliance

Typical Turn Flow

1. UI → Gateway (session validation, authentication)
2. Pre‑guards sanitize input (moderation, PII, injection checks)
3. Orchestrator builds prompt and calls RAG if needed
4. Model generates draft response
5. Post‑guards validate (schema, safety, factuality)
6. Tool execution if required (with result merging)
7. Final answer delivered to UI with citations
8. Logging of traces, evals, and feedback

Performance Targets

• Latency: p95 < 1.5–2.5s with caching + streaming
• Safety: Block reasons surfaced; redaction logs maintained
• Quality: Online eval pass‑rate, helpfulness, citation coverage
• Cost: Tokens/tool calls per turn; caps by plan/tenant

Building Block Behavior

Prompts

• Turn‑scoped templates optimized for responsiveness
• Tool schemas included in‑line for context
• Streaming‑friendly formatting
• A/B testing for optimization

Agents

• Single orchestrator per conversation
• Pre/post processors for input/output shaping
• Hooks like beforePrompt/afterToolCall for telemetry
• Error recovery and retry logic

LLM Guards

• Lightweight, fast pre & post checks every turn
• Interactive fallbacks (ask user to rephrase)
• Real-time feedback on blocked content
• User-friendly error messages

Evals

• Online sampling for quality monitoring
• Helpfulness tracking and coverage metrics
• A/B testing for prompts and models
• Real-time alerts for quality degradation

RAG

• On‑demand retrieval per turn
• Session filters for personalization
• Intelligent caching for performance
• Smaller k for faster responses

Memory

• Conversation + short‑term TTL
• User/profile preferences persistence
• Ephemeral corrections and feedback
• Context summarization for long conversations

Operational Concerns

• p95 latency monitoring and alerting
• Cost per turn tracking and optimization
• Chat observability with conversation flows
• Safety redactions visible to users
• Real-time debugging capabilities

Common Patterns

Multi-Turn Conversations

• Context preservation across turns
• Conversation summarization for long sessions
• Topic switching and context switching
• User intent clarification

Tool Integration

• Parallel tool execution for speed
• Tool result aggregation and ranking
• Fallback strategies for tool failures
• User confirmation for sensitive operations

Error Handling

• Graceful degradation when services fail
• User-friendly error messages
• Retry mechanisms with exponential backoff
• Escalation to human agents when needed

Personalization

• User preference learning
• Conversation history utilization
• Adaptive response styles
• Context-aware recommendations

Next Steps

1. Review the Workflow-Based Architecture to understand the alternative approach
2. Check the Architecture Comparison for detailed trade-offs
3. Start with the core setup checklist for your implementation
4. Plan your monitoring strategy before going live