Introduction
Understanding how AI agents work under the hood helps you make informed decisions about implementation, performance, and scaling. This guide covers the technical architecture powering modern AI agents.
Core Components
1. Vector Databases
Vector databases store embeddings—numerical representations of text that capture semantic meaning. TKC uses Pinecone with 384-dimensional embeddings.
- Query Time: ~0.05 seconds
- Similarity Threshold: 82.4%
- Use Case: Knowledge base retrieval, conversation memory
2. Conversation Persistence
Redis powers state management with LangGraph workflows for complex multi-turn conversations.
- Memory: 1GB per conversation
- Persistence: Cluster-ready, fault-tolerant
- Use Case: Maintaining context across interactions
3. AI Models
Gemini 2.5 Flash models with ReAct patterns enable intelligent reasoning and decision-making.
- Location: us-central1 (production-grade)
- Pattern: ReAct (Reasoning + Acting)
- Use Case: Natural language understanding, response generation
Production Architecture
AI agents run on Cloud Run with:
- Auto-scaling based on demand
- 99.9% uptime SLA
- Global edge caching
- Real-time monitoring and alerting
Performance Metrics
- Response Time: 2-8 seconds average
- Accuracy: 95%+ for common queries
- Cost: $0.001-0.01 per interaction
- Scalability: Handles 10,000+ concurrent conversations
Scaling Strategies
- Horizontal scaling (add more instances)
- Edge caching for common queries
- Batch processing for non-real-time tasks
- Load balancing across regions
Want to learn more about our technical architecture? Book a call with our technical team.