6 posts
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.
Most comprehensive Turkish technical guide for ReAct Pattern (Reasoning + Acting): academic foundation (Yao et al. 2022 ICLR paper), CoT vs ReAct difference, Thought-Action-Observation loop, 5 ReAct variants (Vanilla, MRKL, Self-Ask, ReWOO, Plan-and-Execute), LangChain + LangGraph + LlamaIndex implementations, agentic tool use integration, 25+ Turkish practical examples, error handling, production deployment, observability, cost optimization, model comparison.
A comprehensive Turkish guide to using Anthropic's Claude AI from beginner to advanced. Covers the 1M-context Claude Opus 4.7, Projects, Artifacts, Computer Use, Claude Code, Constitutional AI, MCP integration, plan comparison, and KVKK-compliant strategy for Turkish enterprises in 2026.
A comprehensive 2026 reference explaining how AI agents work, which architectures solve which problems, and what they mean for Turkish enterprises. Covers ReAct, multi-agent, MCP, tool use, computer use, browser agents, frameworks (LangGraph / AutoGen / CrewAI / Claude Code), production concerns, evaluation, security, KVKK compliance, and three anonymized Turkish case studies.
Many organizations turn their first successful experiences with large language models into the mistaken belief that prompt engineering can solve every problem. In reality, while prompt design is a powerful starting point, not every task can be solved by writing better instructions. Multi-step processes require workflows, up-to-date and organization-specific knowledge requires retrieval, and interactions with systems, data sources, or business actions require tool use. This guide explains the limits of prompt engineering in enterprise settings, clarifies when prompting is enough, and shows when workflows, retrieval, or tool use become necessary—and how these layers should work together in production-grade systems.
Building a reliable AI agent is not just about giving a large language model access to tools. Production-grade quality depends on how the agent chooses tools, plans multi-step tasks, manages memory, decides when to involve humans, and how the entire execution flow is observed and governed. This guide explains tool calling, planning, and memory from an enterprise systems perspective, and presents a practical architecture for reliable agentic AI with state management, human-in-the-loop design, observability, security, and governance.