A practical comparison of 12 major AI Agent frameworks and Coding Agent products, covering Agent Loops, context, tools, permissions, subagents, MCP, persistence, sandboxing, and human-in-the-loop design.
Category: AI & Agents
Explore LLMs, AI agents, RAG, MCP, and agent architecture, with a focus on models, context, tools, memory, and practical AI engineering.
AI & Agents
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Chapter 4 — What Is an Agent Harness?
An Agent Harness is the runtime framework that transforms LLM reasoning into reliable, safe, controllable, and stateful real-world actions. This chapter explains the relationship between Model, Agent, and Harness, its core responsibilities, Harness Engineering, production challenges, and why the Harness will remain essential as AI models evolve.
AI & Agents
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Chapter 3 — The Agent Loop: The Core Execution Cycle of an AI Agent
Agent Loop is the core execution cycle of an AI Agent. This chapter explains Observe–Think–Act, ReAct, Tool-use Agents, Self-correction, error handling, context overflow, and why Agent Harness complexity matters.
AI & Agents
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Chapter 2 — LLM Application Architecture: Core Components and Engineering Foundations
Learn the core architecture of LLM applications, including models, prompts, context, tools, memory, state, runtime, Tool Calling, streaming, structured output, error handling, model routing, and cost management.
AI & Agents
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Chapter 1: Rethinking AI Agents
Chapter Objective: Build an accurate understanding of AI Agents, clarify the boundaries between Chatbots, Copilots, Agents, Autonomous Agents, and Coding Agents, understand why an LLM can serve as the “brain” of an Agent, and establish the fundamental definition of an Agent: Agent = LLM + Harness. This chapter provides the conceptual foundation for the entire book