AI Agent Architecture

All major AI agent architectures explained for engineering leaders: Mixture of Experts, multi-agent orchestration, swarm, mesh, pipeline.

Multi-agent orchestration coordinates specialized AI agents. This guide covers centralized and decentralized patterns, state management, error handling.

Compare the 6 leading multi-agent frameworks: OpenAI Agents SDK, LangGraph, CrewAI, AutoGen/AG2, Google ADK.

From prototype to production with multi-agent AI. Covers architecture requirements, state management, observability with distributed tracing.

MCP connects agents with tools. A2A connects agents with each other. Both use JSON-RPC 2.0 and are open standards.

Technical analysis of MoE architecture: sparse activation, routing networks, and expert routing. Real-world figures from DeepSeek-V3, Qwen3-235B, and Mixtral.

EU AI Act fine structure explained: up to 35M euros or 7% of turnover. How penalties are calculated, who enforces them, and what triggers each tier.

AI automation combines machine learning with process automation to handle tasks that need judgment, not just rules. Here is how it works and where it matters.

Hyperautomation combines AI, RPA, process mining, and low-code into a unified automation approach. Here is how it works and how to implement it.