Architecting Microservices for Agentic AI Integration
summary
Practical, production-proven guidance for integrating agentic AI: teams run 40 to 50 agents in production and move from chat-driven fans-out to deterministic, tool-callable APIs using OpenAPI 3.1 and RFC 9457 for stable schemas and machine-readable errors. Implement idempotency keys for POSTs, an outbox/saga async orchestration instead of agent-side choreography, tenant and agent bulkheads, P99 tail latency monitoring via OpenTelemetry, kill switches, per-tenant cost/budget guards, and a dedupe window (30 minutes) plus queueing/202 patterns to avoid retry storms and bill shock.
AI-written summary · about
chapters (9)
- 0:00Introduction to agentic AI microservices
- 7:12Why AI needs control like a car
- 15:21Orchestration agent and prompt templates
- 24:33Anti-patterns in agent API design
- 31:39Reference architecture for agent systems
- 38:43Separating human and agent traffic
- 45:50API lifecycle and budget management
- 55:21Kill switches and duplicate prevention
- 58:50Summary and Q&A
related talks
29mAgentic AI Meets Event-Driven Architecture
22mHOW TO BUILD YOUR OWN AI AGENT: THE COMPLETE ARCHITECTURE FOR ENGINEERING TEAMS
43mArchitecting the Future: Modern Solution Design with Agentic AI
27mMulti-Agent AI: Transforming Business Operations and Value Creation
17mAI TechOps: Data and Infra for AI Foundation to Scale and Manage 1000s of Cohesive Agents
52mDesigning Production-Ready Agentic Systems: Lessons from the Field
25mBeyond Orchestration: An Open Source Architecture for Agentic Systems
39mFrom Demo to Production: Why Agentic AI Systems Fail - and How to Fix Them