Agents

Introduction Tool calling (also called function calling or plugins) is the capability that turns large language models from text predictors into general-purpose controllers for software. Instead of only generating natural language, an LLM can: Decide when to call a tool (e.g., “get_weather”, “run_sql_query”) Decide which tool to call Construct arguments for that tool Use the result of the tool to continue its reasoning or response This post is a deep dive into the anatomy of tool calling: the moving parts, how they interact, what can go wrong, and how to design reliable systems on top of them. ...

Introduction As language models are woven into more complex systems—multi-tool agents, retrieval-augmented generation, multi-model stacks—“what should handle this request?” becomes a first-class problem. That’s what a semantic router solves. Instead of routing based on keywords or simple rules, a semantic router uses meaning (embeddings, similarity, sometimes LLMs themselves) to decide: Which tool, model, or chain to call Which knowledge base to query Which specialized agent or microservice should own the request This post is a detailed, practical guide to semantic routers: ...

As an expert LangChain engineer and educator, I’ll guide you from zero knowledge to hero-level proficiency with the LangChain Cookbook. This practical resource collection offers end-to-end code examples and workflows for building production-ready AI applications using components like RAG (Retrieval-Augmented Generation), agents, chains, tools, memory, embeddings, and databases[1][5][6]. Whether you’re a beginner prototyping in Jupyter or scaling to production, this tutorial provides step-by-step runnable examples, common pitfalls, extension tips, and best practices. ...

As LLM-powered systems have grown more capable, they have also grown more complex. By 2025, most production-grade AI systems no longer rely on a single monolithic agent. Instead, they are composed of multiple specialized sub-agents, each responsible for a narrow slice of reasoning, execution, or validation. Sub-agents enable scalability, reliability, and controllability. They allow systems to decompose complex goals into manageable units, reduce context pollution, and introduce clear execution boundaries. This document provides a deep technical explanation of how sub-agents work, how they are orchestrated, and the dominant architectural patterns used in real-world systems, with links to primary research and tooling. ...

By 2025, Large Language Models (LLMs) have evolved from isolated text-generation systems into general-purpose reasoning engines embedded deeply into modern software systems. This evolution has been driven by: Agentic workflows Retrieval-augmented generation Standardized tool interfaces Long-context reasoning Stronger evaluation and observability layers This article provides a system-level overview of the most important LLM tools and concepts shaping 2025, with direct links to specifications, repositories, and primary sources. 1. Frontier Language Models & Architectural Shifts 1.1 Frontier Closed-Source Models Closed-source models lead in reasoning depth, multimodality, and safety research. ...