Ai Agents

Introduction “AI agents” are everywhere in current discourse: customer support agents, coding agents, research agents, planning agents. But the term is often used loosely, sometimes referring to: A single large language model (LLM) call A script that calls a model and then an API A complex system that plans, acts, remembers, and adapts over time To design, evaluate, or improve AI agents, you need a clear mental model of what an agent actually is and how its parts work together. ...

Introduction The promise of large language models (LLMs) is compelling: intelligent agents that can handle customer interactions, provide guidance, and automate complex tasks. Yet in practice, developers face a critical challenge that no amount of prompt engineering can fully solve. An AI agent that performs flawlessly in testing often fails spectacularly in production—ignoring business rules, hallucinating information, and delivering inconsistent responses that damage brand reputation and customer trust.[3] This gap between prototype and production is where Parlant enters the picture. Built by Emcie, a startup founded by Yam Marcovitz and staffed by engineers and NLP researchers from Microsoft, Check Point, and the Weizmann Institute of Science, Parlant is an open-source framework that fundamentally rethinks how developers build conversational AI agents.[3] Rather than fighting with prompts, Parlant teaches agents how to behave through structured, programmable guidelines, journeys, and guardrails—making it possible to deploy agents at scale without sacrificing control or compliance.[3] ...

Vercel’s AI SDK 6 beta introduces groundbreaking features like tool execution approval, a new agent abstraction, and enhanced capabilities for building production-ready AI applications across frameworks like Next.js, React, Vue, and Svelte.[1][5] This release addresses key pain points in LLM integration, such as safely granting models powerful tools while abstracting provider differences.[1][3] What is the Vercel AI SDK? The AI SDK is a TypeScript-first toolkit that simplifies building AI-powered apps by providing a unified interface for multiple LLM providers, including OpenAI, Anthropic, Google, Grok, and more.[3][4] It eliminates boilerplate for chatbots, text generation, structured data, and now advanced agents, supporting frameworks like Next.js, Vue, Svelte, Node.js, React, Angular, and SolidJS.[3][4][6] ...

Large Language Models (LLMs) excel at processing text, but they lack real-time web access. By integrating browsers, developers can empower LLMs to fetch live data, automate tasks, and interact dynamically with websites. This zero-to-hero tutorial covers core methods—browser APIs, web scraping, automation, and agent pipelines—with practical Python/JS examples using tools like LangChain, Playwright, Selenium, and more. Why Browsers + LLMs? Key Use Cases Browsers bridge LLMs’ knowledge gaps by enabling: ...

Introduction The rise of AI agents has created a fundamental challenge: how do you connect dozens of LLMs to hundreds of external tools without writing custom integrations for every combination? This is the “N×M problem”—managing connections between N models and M tools becomes exponentially complex. The Model Context Protocol (MCP) solves this by providing a standardized interface between AI systems and external capabilities. Docker’s integration with MCP takes this further by containerizing MCP servers, adding centralized management via the MCP Gateway, and enabling dynamic tool discovery. ...