5 posts
What is an AI agent? AI agents have become one of the most discussed topics in modern AI. But for most organizations, the real question remains: what is the difference between simple workflow automation and a truly agentic system? Is every LLM-powered automation an agent, or do agentic systems requi
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.
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.
As AI agent systems become more common, one of the most important architectural questions is whether to use a single powerful agent or distribute tasks across multiple specialized agents. Many teams assume multi-agent systems are automatically more advanced, leading to unnecessary complexity. Others force truly separable workflows into a single agent and lose quality, control, and scalability. This guide compares single-agent and multi-agent architectures across technical, operational, cost, security, observability, coordination, and governance dimensions, and explains how to choose the right architecture for the right enterprise problem.
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.