How do you design AI agent architectures that scale to enterprise workloads?

Enterprise AI agent architecture requires horizontal scaling, reliability, and governance. Core components: API gateway (rate limiting, authentication, routing), agent orchestrator (manages agent lifecycle, routes requests to appropriate agents), tool execution layer (sandboxed environments for tool calls), memory service (centralized state management), and observability platform (tracing, logging, metrics). Scaling strategy: stateless agent workers that scale horizontally behind a load balancer, with state externalized to Redis (working memory) and PostgreSQL (long-term memory). Use message queues (Kafka, SQS) to decouple request intake from agent execution for async processing. Multi-tenancy: isolate tenant data, enforce per-tenant rate limits, and use separate tool credentials per tenant. Reliability: implement circuit breakers for LLM API calls, fallback to alternative models when primary is unavailable, retry with exponential backoff, and maintain a request queue for graceful degradation under load. Governance: role-based access control for agent capabilities, approval workflows for high-risk actions, comprehensive audit logging, cost allocation per tenant/department, and model version management. Monitoring: track latency per agent step, token usage and costs, tool call success rates, and business outcome metrics (resolution rate, user satisfaction). Target SLAs: P95 response under 5 seconds for simple queries, 99.9% availability during business hours.