LLM Integration

Introduction The last few years have witnessed a dramatic shift from static, rule‑based bots to agentic systems—autonomous software entities that can reason, plan, and act on behalf of users. While the term “agent” is often used loosely, a true agent exhibits three core capabilities: Goal‑oriented behavior – it knows what it wants to achieve. Dynamic planning – it can break the goal into steps, adapt when conditions change, and recover from failures. Tool use – it can invoke external APIs, run code, or interact with other services to fulfill its plan. The Open-Action Protocol (OAP) 2.0—released in early 2026—was designed explicitly to make the construction of such agents easier, more interoperable, and safer. In this article we will explore why OAP 2.0 matters, how it differs from the original version, and walk through a complete end‑to‑end implementation of an agentic workflow that goes far beyond a simple chatbot. ...

Introduction The rapid evolution of large language models (LLMs) has turned prompt engineering into a mainstream practice. Early‑stage developers often treat an LLM as a sophisticated autocomplete engine: feed it a carefully crafted prompt, receive a text response, and then act on that output. While this “prompt‑then‑act” loop works for simple question‑answering or single‑turn tasks, it quickly breaks down when we ask an LLM to operate autonomously—to plan, execute, and adapt over many interaction cycles without human supervision. ...