A principal cloud architect's guide to Google's Agent-to-Agent (A2A) protocol: how it works, how it differs from MCP, and how to deploy multi-agent systems across AWS Bedrock AgentCore, Azure AI Foundry, and Google Cloud in production.
How to design production-grade memory systems for AI agents: episodic, semantic, and procedural memory, vector and graph storage, mem0, Bedrock AgentCore Memory, and the governance questions every team ignores until it's too late.
AI agents face a new class of security threat that breaks every assumption from traditional application security. A principal cloud architect's guide to prompt injection defense, tool access controls, output filtering, and the layered guardrails architecture that actually protects production AI systems.
A principal cloud architect's guide to choosing between LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK for production agentic AI systems. Covers state management, cost control, and deployment architecture.
MCP is the protocol that lets AI agents use tools, query databases, and call APIs. Here's how the architecture works, why it matters, and how to deploy it in production.
MCP is the protocol that lets AI agents talk to databases, APIs, and cloud services without custom glue code. Here's how it works architecturally, how to deploy MCP servers in production, and what the security model actually looks like.
The real challenges of running agentic AI systems in production: non-determinism, token cost spirals, observability gaps, and how to solve them.
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