3 posts
Anthropic's Multi-Agent Research system beat single-agent Claude Opus by 90.2% on internal research evals using an orchestrator-worker pattern. This guide covers lead agent + parallel subagent architecture, structured artifact handoffs, planner-generator-evaluator loops, Claude Agent SDK with .claude/agents/, cost caps, deadlock prevention, comparisons with CrewAI/LangGraph/AutoGen, and a Turkish law-firm contract-analysis case.
Launched by Anthropic in November 2024, the Model Context Protocol (MCP) became the 'USB-C of AI' in 2026 — with 5,000+ servers, adoption by OpenAI, Google, Microsoft, and Salesforce, and its own Wikipedia page. This guide covers MCP's three primitives, transport layer, OAuth 2.1, building your own server, security risks, and Turkish-market opportunities for Yargıtay/BIST/KVKK-style MCPs.
Enterprise AI architecture is not just about selecting a large language model. A reliable AI system requires data pipelines, model infrastructure, API integrations, security controls, observability, workflow orchestration, human approval mechanisms and governance layers. This guide explains how to design production-ready enterprise AI systems from a strategic and technical perspective.