How to Build AI &
Agentic AI Systems
Architecture and patterns for designing production-grade agentic AI — from orchestration and memory to safety, observability, and cloud deployment.
Connect for Speaking EngagementArchitecture
The Six Pillars of Production Agentic AI
Every production-grade agentic AI system rests on these six architectural layers. Skipping any one leads to systems that fail in ways that are hard to debug and expensive to fix.
Orchestration & Planning
Decompose complex tasks with LangGraph DAG workflows, AutoGen reflexion loops, or CrewAI role-based delegation. Choose the right pattern for your latency and reliability requirements.
Memory Architecture
Design episodic (event history), semantic (knowledge base), and procedural (skill) memory layers. Decide what to persist, what to retrieve, and when to forget.
Tool & API Integration
Connect agents to the world via MCP for tool discovery, A2A for inter-agent delegation, and function calling for structured API interaction — with strict input validation.
Safety & Governance
Implement least-privilege scoping, prompt injection defence, human-in-the-loop approval gates, and irreversible-action guardrails before deploying to production.
Observability & Evaluation
Instrument every agent step with trace IDs, measure task completion and faithfulness, detect drift, and build evaluation harnesses for non-deterministic workflows.
Cloud & Deployment
Serve production agents on Kubernetes with vLLM or Triton for inference, auto-scaling for bursty load, and multi-region deployment for data-residency compliance.
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