9 posts
The secret to agentic AI success is discipline, not the model. Four bottlenecks in pilot-to-production, maturing frameworks, and KVKK/EU AI Act governance.
The number-one cause of AI agent failure is memory. A field guide to short/long/graph memory architectures, multi-scope patterns, LangGraph, and KVKK-compliant retention for 2026.
What is a multi-agent system? A multi-agent system is an architecture where several AI agents, each with its own role, solve a task together by dividing the work and communicating with one another. This guide: a clear definition, the difference from a single agent, how it works, agent orchestration, task division, LangGraph and CrewAI, real-world examples, limits, and FAQs.
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.
Prompt engineering is dead, context engineering is alive. Anthropic's 90% cost-cutting prompt caching, GPT-5.5's 272K input threshold, Claude Opus 4.7's 1M context, and agent runtime state management are rewriting AI engineering in 2026. Turkish token efficiency, KVKK-compliant state stores, the 'Don't Break the Cache' principle.
Naive RAG's six fatal weaknesses are fully solved in 2026 by agentic RAG. A production-grade RAG with plan/reflect/verify loops, hybrid retrieval, and claim-verification built on the LangGraph v0.4 state-graph used by Klarna, LinkedIn, and Uber — plus a KVKK-compliant Turkish bank case study and cost-latency tradeoffs.
Most comprehensive Turkish technical guide for Tree of Thoughts (ToT): academic foundation (Yao et al. 2023 NeurIPS paper), CoT vs ToT vs GoT comparison, search algorithms (BFS, DFS, Beam Search, A*), 4 ToT components, classic benchmark results, 25+ Turkish practical examples, LangGraph implementation, cost analysis, Graph of Thoughts evolution, agentic systems integration.
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 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.