LangChain

Build an AI Agent with the Model Context Protocol (MCP)

This technical step-by-step guide explains how to build a simple AI agent that uses the Model Context Protocol (MCP) to call external tools and return structured, reliable responses. The tutorial uses a weather-tool example implemented with the MCP Python SDK and FastMCP, demonstrates the request flow between User → Claude Desktop → MCP Client → MCP Server → Tool → Claude → User, lists prerequisites (Python 3.11+, Claude Desktop, Visual Studio Code) and provides runnable example code. It also outlines common beginner mistakes (e.g., returning unstructured text, poor error handling) and suggests next steps such as integrating real APIs, databases, or multi-agent orchestration with LangGraph.

Forcing 1024‑Dim Embeddings Cut Pinecone Costs ~33%

A developer case study published on DEV (Jul 5, 2026) explains how the FastRAG team reduced Pinecone vector-store costs by roughly one third by forcing embedding vectors to 1024 dimensions at ingestion. The article notes storage costs in vector databases scale linearly with embedding dimensionality, that many models default to 1536+ dimensions, and argues 1024 is a practical truncation point that preserves retrieval quality for chunk-level RAG use cases while materially lowering storage bills. FastRAG enforces the truncation during embedding generation (in lib/vector-store.ts) to avoid mixed-dimension indexes and to keep cost savings consistent across uploads. The post frames embedding dimensionality as a simple configuration decision with significant unit-economics impact for document-chat / RAG products.

OWASP Agentic AI Top 10 and Defenses

Agentic AI — LLM-powered systems that autonomously act against external tools and APIs — introduces operational security risks distinct from non-agentic LLM apps. The OWASP Agentic AI Top 10 (published early 2026) enumerates ten primary risk categories (AAI01–AAI10). The AWS Agentic AI Security Scoping Matrix (published November 21, 2025) frames agent risk by resource scope versus action reversibility. Defensive patterns that work in production include scope limitation, action mediation (policy checks), out-of-band confirmations for high-impact actions, per-user identity propagation, comprehensive observability, and continuous red‑teaming. Anthropic’s published research on browser‑control agents provides concrete mitigations for indirect prompt injection. The article positions agentic security as an extension of existing application/LLM security practices and emphasizes deliberate design choices (narrow scope, reversible actions) and continuous adversarial testing for safe deployments.